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300-635 approach - Automating Cisco Data Center Solutions (DCAUTO) Updated: 2024

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Exam Code: 300-635 Automating Cisco Data Center Solutions (DCAUTO) approach January 2024 by Killexams.com team

300-635 Automating Cisco Data Center Solutions (DCAUTO)

300-635 DCAUTO

Certifications: CCNP Data Center, Cisco Certified DevNet Professional, Cisco Certified DevNet Specialist - Data Center
Automation and Programmability

Duration: 90 minutes



This test tests your knowledge of implementing data center automated solutions, including:

Programming concepts

Orchestration

Automation tools



Exam Description

The Automating and Programming Cisco Data Center Solutions v1.0 (DCAUTO 300-635) test is a 90-minute test associated with the CCNP Data Center, Cisco Certified DevNet Professional, and Cisco Certified Specialist - Data Center SAN Implementation certifications. This test tests a candidate's knowledge of implementing Data Center automated solutions, including programming concepts, orchestration and automation tools.



10% 1.0 Network Programmability Foundation

1.1 Utilize common version control operations with git: add, clone, push, commit, diff, branching, merging conflict

1.2 Describe characteristics of API styles (REST and RPC)

1.3 Describe the challenges encountered and patterns used when consuming APIs synchronously and asynchronously

1.4 Interpret Python scripts containing data types, functions, classes, conditions, and looping

1.5 Describe the benefits of Python virtual environments

1.6 Explain the benefits of using network configuration tools such as Ansible and Puppet for automating data center platforms

30% 2.0 Controller Based Data Center Networking

2.1 Describe the following:

2.1.a ACI target policy

2.1.b ACI application hosting capabilities

2.1.c Implementation of an ACI application from the Cisco ACI Apps Center

2.2 Leverage the API inspector to explore the REST API calls made by the ACI GUI

2.3 Construct a Python script to create an application policy using the ACI REST API

2.4 Construct a Python script to create an application policy using the ACI Cobra SDK

2.5 Construct an Ansible playbook to create an application policy

2.6 Describe the benefits of integrating Kubernetes infrastructure using the ACI CNI plugin

30% 3.0 Data Center Device-centric Networking

3.1 Describe Day 0 provisioning with NX-OS

3.1.a Cisco POAP

3.1.b NX-OS iPXE

3.2 Implement On-Box Programmability and Automation with NX-OS

3.2.a Bash

3.2.b Linux containers (LXC and Docker using provided container

3.2.c NX-OS guest shell

3.2.d Embedded Event Manager (EEM)

3.2.e On-box Python Scripting

3.3 Compare model-driven telemetry such as YANG Push and gRPC to traditional network monitoring strategies such as SMNP, Netflow, and SYSLOG

3.4 Construct Python script that consumes model-driven telemetry data with NX-OS

3.5 Implement Off-Box Programmability and Automation with NX-OS

3.5.a Nexus NX-API (NX-API REST and NX-API CLI)

3.5.b Nexus NETCONF using native and OpenConfig

3.5.c Network configuration tools with NX-OS (Ansible)

30% 4.0 Data Center Compute

4.1 Configure Cisco UCS with developer tools

4.1.a UCS PowerTool

4.1.b UCS Python SDK

4.1.c Ansible

4.2 Describe the capabilities of the DCNM API

4.3 Identify the steps in the Intersight API authentication method

4.4 Construct an Intersight API call given documentation to accomplish tasks such as manage server policies, service profiles, and firmware updates

4.5 Describe the process to implement workflows for physical and virtual infrastructure using UCS Director

4.5.a Pre-defined tasks

4.5.b Custom tasks

4.5.c Script libraries

4.6 Utilize UCS Director REST API browser

Automating Cisco Data Center Solutions (DCAUTO)
Cisco Automating approach

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300-635
Automating Cisco Data Center Solutions (DCAUTO)
http://killexams.com/pass4sure/exam-detail/300-635
QUESTION 52 Which authentication method is used when the REST API of the Cisco UCS
Director is accessed?
A. Bearer ((Bearer Token))
B. HTTP Basic Auth
C. RestAuth: ((User's Auth Token))
D. X-Cloupia-Request-Key: ((User's Auth Token))
Answer: B
Section: Data Center Compute
QUESTION 53
DRAG DROP
Drag and drop the items to complete the request to retrieve the current firmware of Cisco UCS devices from the Cisco Intersight API. Not all items are used.
Select and Place:
Answer:
Section: Data Center Compute
Reference: https://developer.cisco.com/codeexchange/github/repo/CiscoUcs/intersight-python/
QUESTION 54
Refer to the exhibit.
Which Ansible module is needed in line 8 to create a new VLAN 10 on the hosts defined in the "ucs" group?
A. vlan
B. ucs_vlans
C. vlans
D. nxos_vlans
Answer: B
Section: Data Center Compute
QUESTION 55
DRAG DROP
A co-worker is using Cisco Intersight to determine the maximum available memory per server for their company's data center. Drag and drop the code to complete the Cisco Intersight API call that provides the desired results. Not all options
are used.
Select and Place:
Answer:
Section: Data Center Compute
QUESTION 56
Which two components are required from the Cisco Intersight REST API Authentication? (Choose two.)
A. SHA256 hash of the message body and message headers.
B. SHA256 hash of the message body, including empty message bodies.
C. RSA private key with a key size of 2048.
D. RSA private key with a key size of 1024.
E. SHA384 hash of the message body, excluding empty message bodies.
Answer: AC
Section: Data Center Compute
QUESTION 57 Which two statements apply to authentication when using the Cisco Intersight
API? (Choose two.)
A. Each API Key can be assigned specific roles but not privileges.
B. Secret Key is only available at API Key creation time.C. An API Key is composed of a Key ID and Secret Key.
D. The user credentials for the cisco.com accounts are shared with the Cisco Intersight Web Service.
E. An API Key is composed of a keyId and sessionCookie.
Answer: BC
Section: Data Center Compute
QUESTION 58 Which two statements describe the authentication method used with Cisco Intersight REST API
Requests? (Choose two.)
A. The REST API request contains a base64-encoded signature of the message content and headers.
B. The REST API request message body is encoded as a SHA384 hash and then signed with the API Key ID.
C. The Cisco Intersight Web service verifies the signature of incoming request with the RSA public key for the API Key ID.
D. The incoming REST API request is challenged by the Cisco Intersight Web service with a request for the RSA private key.
E. The message body is encoded as a SHA256 hash if the message body is not empty and then signed with the API Key ID.
Answer: AD
Section: Data Center Compute
QUESTION 59
Refer to the exhibit.
Cisco Intersight has an NTP server policy called My_NTP_Policy configured that contains a single NTP server pool entry "pool.ntp.org".
Which Cisco Intersight API call adds an additional NTP server (10.20.0.1) to the My_NTP_Policy server policy? A.
B.
C.
D.
Answer: B
Section: Data Center Compute
QUESTION 60
A server profile with the string "WEST15" in its name must have the string "WEST15" changed to "LXT14". For example, server profile "VMHOST-WEST15-01" would need to be changed to "VMHOST-LXT14-01".
Using the Cisco Intersight REST API in a Python script, which two GET API requests are used to retrieve just the server profile with the string "WEST15" in the name and the correct body for the API request to update the name? Assume the
variable "sp_name" contains the name of the retrieved server profile. (Choose two.)
A. GET https://intersight.com/api/v1/server/Profiles?$select=Name&$filter=contains(Name, 'WEST15')
B. GET https://intersight.com/api/v1/server/Profiles?$select=Name&$filter=Name in('WEST15')
C. BODY = { "Name": sp_name.format('WEST15', 'LXT14') }
D. GET https://intersight.com/api/v1/server/Profiles?$select=Name&$filter=startswith(Name, 'WEST15')E. BODY = { "Name": sp_name.replace('WEST15','LXT14') }
Answer: AE
Section: Data Center Compute
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Cisco Automating approach - BingNews https://killexams.com/pass4sure/exam-detail/300-635 Search results Cisco Automating approach - BingNews https://killexams.com/pass4sure/exam-detail/300-635 https://killexams.com/exam_list/Cisco Cisco’s Approach To Simplifying Security Technologies

Cisco says its comprehensive platform approach helps partners deliver a simpler security solution to customers.

Security was a hot syllabu at this week’s Cisco Partner Summit 2019 as partners look to secure their and their customers’ futures in a complex, multi-vendor environment.

“We’re integrating the portfolio together so that it simplifies the engagement model with our partners,” said Gee Rittenhouse, senior vice president and general manager of security at Cisco.

In other words, partners wouldn’t have to integrate individual point products to provide a full security solution.

Cisco is also combining analytics and automation on top of its security offerings to simplify the operational side as well, Rittenhouse said.

As a partner, Bob Rossi, vice president of networking, collaboration and security at CDW, said it can be an “everyday challenge” to provide a simple security solution to customers.

“A lot of our customers have gone with a best-of-breed type of approach and they have all these different point products from different partners, and they’re really struggling today to come up with a way to manage all of these alerts and budgets: ‘What do I pay attention to? What’s really important?’” said Rossi.

With Cisco’s latest technologies, Rossi said, he has seen a more simplified approach, “giving customers a much easier environment to manage.”

New services like Cisco Threat Response simplifies a security team’s breach defense experience with more visibility and actions for threat response, “taking intelligence from three different feeds, from email, from endpoint and firewall, [to help customers] understand what threats exist in our environment,” said Rossi.

Cisco is also leading the way the in the “Zero-Trust” security model.

What sets Cisco apart, said Rittenhouse, is having a “comprehensive view of Zero Trust.”

“Instead of just taking a singular product and then adding Zero Trust to it, we think about it as Zero Trust for the workforce, the workplace and the workload, all of which is necessary to have a true Zero Trust solution.”

Watch CRNtv to hear more from Rittenhouse and Rossi.

Fri, 08 Nov 2019 08:02:00 -0600 text/html https://www.crn.com/news/security/cisco-s-approach-to-simplifying-security-technologies
Cisco Partner Summit: IT Platform Approach Will Make IT ‘Faster’ And ‘Smarter’

‘The focus for Cisco overall is to build these agile platforms for IT to work faster and smarter, and we are building this across every aspect of the infrastructure, including users and applications,’ Cisco’s Prashanth Shenoy tells CRN.

With simplicity and agility top of mind for Cisco Systems, the tech giant unveiled its new IT platform strategy for moving and automating workloads and applications on Wednesday during Cisco Partner Summit Digital 2020.

The new approach will help customers work faster and smarter, especially at a time when resources are scarce and IT requirements have changed overnight, said Prashanth Shenoy, vice president of marketing, Enterprise, Data Center and Cloud Networking, IoT, and Developer Platform for Cisco.

Specifically, when it comes to the hybrid cloud approach. “We’re living in challenging environments, especially IT, which is being tasked with driving new projects in a very rapid manner that no one expected three quarters ago,” Shenoy said. “IT projects are being approved and implemented in weeks, rather than a year and the amount of work that IT has to do to drive that is immense.”

[Related: Cisco DevNet Specialization Fast-Tracked To Help Partners In A Post-COVID World]​

Cisco revealed three new and updated solutions as part of its platform approach, including new features for systems management platform Cisco Intersight, A Cisco Nexus Dashboard, and an updated Cisco Identity Services Engine (ISE). The updates will help businesses that are re-thinking their tooling and IT operations, Shenoy said.

The Cisco Intersight hybrid cloud platform that connects private data centers to private clouds has been extended to offer an Intersight Kubernetes Service, which will let infrastructure teams automate the lifecycle management of Kubernetes and containerized applications across any environment. It’s not just developers that need to understand Kubernetes and containers anymore, said Todd Brannon, senior director, data center marketing for Cisco. “We’re seeing Kubernetes and containers go mainstream. It’s not just the DevOps teams that need to be adept -- we’re seeing the core IT teams being tasked with operationalizing these environments and for many, these are relatively new stacks of software,” he said. The new offering will let businesses handle containers as a service in a predictable and consistent way, Brannon said.

Intersight Workload Optimizer, a new service available now, simplifies application resources management and telemetry data into a single tool that lets customers balance application performance and cost. Additionally, Cisco unveiled Intersight integration with AppDynamics, an integrated service with Workload Optimizer, which gives IT teams visibility into cloud-native applications to Excellerate management across their infrastructure and stay ahead of issues, Brannon said.

“We’ve set a very ambitious goal for ourselves to build out Intersight as an all-in platform for hybrid IT, because hybrid cloud can be hard, but it doesn’t have to be,” he said.

Over in the data center, the new Cisco Nexus Dashboard provides insights and automation to operate multi-cloud data center networks, including on-premise, virtual edge and cloud sites. The new dashboard consolidates Cisco’s portfolio of operational services and critical third-party services to supply customers one place to manage application lifecycles, according to the San Jose, Calif.-based company.

The updated Cisco ISE now simplifies secure network access across all domains, extending the zero-trust workplace to anywhere and on anything. Businesses can now use ISE to identify a variety of IoT endpoints to enforce consistent policies from the cloud to supply teams the agility and flexibility they need to secure their organizations, Cisco said.

“The focus for Cisco overall is to build these agile platforms for IT to work faster and smarter, and we are building this across every aspect of the infrastructure, including users and applications,” Shenoy said.

The new open platform approach will supply Cisco partners the opportunity to layer their own services on top and integrate with third-party technology, Shenoy added. “Since all of these are software platform, it will help [partners] drive that recurring revenue stream and profitably software businesses,” he said.

The new Cisco Intersight features and the Cisco Nexus Dashboard will be available by the end of the calendar year, with the Cisco Intersight Kubernetes Service becoming available in the first half of 2021. The updated Cisco ISE is available now, according to Cisco.

Fri, 08 Dec 2023 16:16:00 -0600 text/html https://www.crn.com/news/networking/cisco-partner-summit-it-platform-approach-will-make-it-faster-and-smarter
Cisco: The Power of Purpose

Published 12-28-23

Submitted by Cisco Systems, Inc.

child and adult with technology between them

We released our annual Purpose Report, which reflects and celebrates the past year’s work towards Powering an Inclusive Future for All—the progress we’ve made against our goals, and the people and lives we’ve touched. The report explores the theme, The Power of Purpose, because we recognize that when we intersect our business, technology, and a network of partners together with our purpose, we create a powerful force for lasting change. And we have some incredible examples from this year, including the announcement that we achieved our goal of positively impacting 1 billion lives, and did so over a year early!

For many years, the purpose of our Purpose Report has been to look back. But we must also look ahead.

Any company looking to successfully execute their business strategy must consider the changing terrain, identify upcoming challenges and trends, and anticipate how to best meet evolving requirements. The same is true for purpose. This year’s Purpose Report begins to explore the landscape, and where we see opportunities for Purpose to grow.

Our biggest challenges are interconnected and interdependent

The past several years brought us all unprecedented challenges, and a world more prone to polarization than before. But instead of binary questions and issues, a more multipolar world has emerged, requiring us to operate with more nuance and greater context than ever. In this context one thing is clear—we are more interconnected and interdependent than ever.

Our lives and futures are linked by our shared dependence on our planet and its environments. We have a global responsibility to solve the climate crisis together. We see the growth of an increasingly digital and global economy, keeping us connected through ecosystems of financial interdependence. And as we learned in the exact pandemic, our collective health is also inextricably linked.

Global crises also continue to grow increasingly interconnected­­––and the consequences disproportionately fall on vulnerable communities. Developing nations who often contribute the least to climate change bear the brunt of its impact. And due to a lack of infrastructure and technological advancement, they are often the least equipped to respond to natural disasters. While the digital economy continues to grow, 2.6 billion people remain unconnected, denying them access to the opportunities and resources available. The consequences of each crisis exacerbate others­­—access to education is disrupted, progress for women and girls is set back, and extreme poverty rates rise.

Pursuing our Purpose can and must be the glue that brings us together to meet this moment and address these complex, interconnected issues. The question we must continue to ask as we look ahead is, how?

This year’s report reflects on howhow we achieved our goal of positively impacting 1 billion lives, how the private sector can work in new ways to address critical issues facing our societies, and how we can apply lessons from the past to build resilience in our communities for the future.

Where do we go from here?

There is no doubt that the path forward for business in a multipolar world isn’t entirely clear. There is significant work ahead to address risks in supply chains and manufacturing, and complex questions on how to best navigate a shifting geopolitical terrain. But should these challenges and uncertainties also apply to Purpose?

I don’t think so. In fact, in this moment when many are shying away from a global mindset and approach, our Purpose work proceeds by pursuing what is most meaningful, regardless if that is at the local or global level. Purpose can flex. It operates in a lane that is valued around the world, giving all of us who do this work the space to create and iterate, to sway and pivot, and find our rhythm. And when we do, pursuing our Purpose holds the door open for economic initiatives.

As we close the year in which we reached a goal of positively impacting one billion people, I’m looking ahead and considering the next goal we’ll set for ourselves. We are stronger with our partners by our side—an ecosystem focused on driving impact. We’ll continue to do this if we integrate the lessons of the past and take a new approach in the days and years ahead. I hope you’ll join us on this journey and read about our impact this year, and my reflections on what’s next, in our FY23 Purpose Report. Together, we can do good for our communities, good for our businesses, and good for all.

Read the full Cisco FY23 Purpose Report

View original content here.

Thu, 28 Dec 2023 01:11:00 -0600 en text/html https://www.csrwire.com/press_releases/791941-cisco-power-purpose
2023: The Year Generative AI Transformed Enterprise Data Management

As we transition from one year to the next, it's a season of reflection and looking forward. As an analyst, the end of the year is a time to learn from past work, analyze its outcomes and consider its potential impact on the future.

In 2023, enterprise data management (EDT) solutions underwent significant changes due to the influx of generative AI technologies. These technologies have fundamentally altered how businesses approach data management, analysis and usage. In this post, I’ll review some of 2023’s highlights in this field.

How Different Areas Of EDT Are Evolving

Over the past year, there have been promising developments in EDT across several key areas. These include data management itself, where the focus has been on using AI to Excellerate how data is organized and accessed. The data cloud sector has also experienced growth, with more businesses adopting cloud-based solutions because of their flexibility, scalability and facility for integrating tools that handle unstructured data.

In data protection and governance, there has been a continuous effort to enhance security measures to safeguard sensitive information. Database technologies have also improved, particularly in handling and processing large data volumes more efficiently by incorporating generative AI.

Recent advancements in data integration and intelligent platforms have been geared towards better aggregating data from multiple sources, allowing for more comprehensive data analysis. The integration of AI and ML has further enhanced the capabilities of these platforms, improving data analysis interpretation and offering more profound and insightful analytical outcomes.

Full disclosure: Amazon Web Services, Cisco Systems, Cloudera, Cohesity, Commvault, Google Cloud, IBM, LogicMonitor, Microsoft, MongoDB, Oracle, Rubrik, Salesforce, Software AG, Splunk, and Veeam are clients of Moor Insights & Strategy, but this article reflects my independent viewpoint, and no one at any client company has been given editorial input on this piece.

Bringing AI To Data Management—And Vice Versa

“In a way, this AI revolution is actually a data revolution,” Salesforce cofounder and CTO Parker Harris said during his part of this year’s Dreamforce keynote, “because the AI revolution wouldn't exist without the power of all that data.” Harris's statement emphasizes the vital role of data in businesses and points to the increasing necessity for effective data management strategies in 2024.

As data becomes more central, the demand for scalable and secure EDT solutions is rising. My exact series of articles focusing on EDT began with an introductory piece outlining its fundamental aspects and implications for business operations. This was followed by a more in-depth exploration of EDT, particularly highlighting how it can benefit businesses in data utilization. These articles elaborated on the practical uses and benefits of EDT and its importance in guiding the strategies and operations of modern businesses.

As businesses continue to leverage generative AI for deeper insights, the greater accessibility of data is set to revolutionize how they manage information. This development means enterprises can now utilize data that was previously inaccessible—a move that highlights the importance of data integration for both business operations and strategic decision-making. For instance, untapped social media data could offer valuable customer sentiment insights, while neglected sensor data from manufacturing processes might reveal efficiency improvements. In both cases, not using this data equates to a missed opportunity to use an asset, similar to unsold inventory that takes up space and resources without providing any return.

Revolutionizing Data Cloud Platforms

Incorporating AI into data cloud platforms has revolutionized processing and analyzing data. These AI models can handle vast datasets more efficiently, extracting previously unattainable insights due to the limitations of traditional data analysis methods.

Over the year, my own collaborations with multiple companies suggested the range of technological progressions. As I highlighted in a few of my articles, Google notably improved its data cloud platform and focused on generative AI with projects including Gemini, Duet AI and Vertex AI, reflecting its solid commitment to AI innovation. Salesforce introduced the Einstein 1 Platform and later expanded its offerings with the Data Cloud Vector Database, providing users with access to their unstructured enterprise data, thus broadening the scope of their data intelligence. IBM also launched watsonx, a platform dedicated to AI development and data management. These moves from major tech firms reflect a trend towards advanced AI applications and more sophisticated data management solutions.

At the AWS re:Invent conference, I observed several notable launches. Amazon Q is a new AI assistant designed for business customization. Amazon DataZone was enhanced with AI features to Excellerate the handling of organizational data. The AWS Supply Chain service received updates to help with forecasting, inventory management and provider communications. Amazon Bedrock, released earlier in the year, now includes access to advanced AI models from leading AI companies. A new storage class, Amazon S3 Express One Zone, was introduced for rapid data access needs. Additionally, Amazon Redshift received upgrades to Excellerate query performance. These developments reflect AWS's focus on integrating AI and optimizing data management and storage capabilities.

Recent articles have highlighted Microsoft's role in the AI renaissance, one focusing on the launch of Copilot as covered by my colleagues at Moor Insights & Strategy, and another analyzing the competitive dynamics in the AI industry. Additionally, Microsoft has expanded its data platform capabilities by integrating AI into Fabric, a comprehensive analytics solution. This suite includes a range of services including a data lake, data engineering and data integration, all conveniently centralized in one location. In collaboration, Oracle and Microsoft have partnered to make Oracle Database available on the Azure platform, showcasing a strategic move in cloud computing and database management.

Automating Data Protection And Governance

With the growing importance of data privacy and security, AI increasingly enables the automation of data governance, compliance and cybersecurity processes, reducing the need for manual oversight and intervention. This trend comes in response to the rise in incidents of data breaches and cyberattacks. AI-driven systems have become more proficient at monitoring data usage, ensuring adherence to legal standards and identifying potential security or compliance issues. This makes them a better option than traditional manual approaches for ensuring data safety and compliance.

Security is not only about protecting data but also about ensuring it can recover quickly from any disruptions, a quality known as data resilience. This resilience has become a key part of security strategies for forward-thinking businesses. Veeam emphasized “Radical Resilience” when it rolled out a new data protection initiative focused on better products, improved service and testing, continuous releases and greater accountability. Meanwhile, Rubrik introduced its security cloud, which focuses on data protection, threat analytics, security posture and cyber recovery. Cohesity, which specializes in AI-powered data security and management, is now offering features such as immutable backup snapshots and AI-driven threat detection; in 2023, it also unveiled a top-flight CEO advisory council to influence strategic decisions. Commvault has incorporated AI into its services, offering a new product that combines its SaaS and software data protection into one platform.

LogicMonitor upgraded its platform for monitoring and observability to include support for hybrid IT infrastructures. This enhancement allows for better monitoring across an organization's diverse IT environments. Additionally, Cisco has announced its intention to acquire Splunk. This acquisition will integrate Splunk's expertise in areas such as security information and event management, ransomware tools, industrial IoT vulnerability alerting, user behavior analytics and orchestration and digital experience monitoring that includes visibility into the performance of the underlying infrastructure.

Key Changes for Database Technology

Advancements in AI and ML integration are making database technology more intuitive and efficient. Oracle Database 23c features AI Vector Search, which simplifies interactions with data by using ML to identify similar objects in datasets. Oracle also introduced the Fusion Data Intelligence Platform, which combines data, analytics, AI models and apps to provide a comprehensive view of various business aspects. The platform also employs AI/ML models to automate tasks including data categorization, anomaly detection, predictive analytics for forecasting and customer segmentation, workflow optimization and robotic process automation.

In my previous discussion about IBM's partnership with AWS, a major highlight is the integration of Amazon Relational Database Service with IBM Db2. This collaboration brings a fully managed Db2 database engine to AWS's infrastructure, offering scalability and various storage options. The partnership between AWS and IBM will likely grow as the trend of companies forming more integrated and significant ecosystems continues.

Database technology also evolved with MongoDB queryable encryption features for continuous data content concealment. MongoDB Atlas Vector Search now also integrates with Amazon Bedrock, which enables developers to deploy generative AI applications on AWS more effectively. It’s also notable that Couchbase announced Capella iQ, which integrates generative AI technologies that exploit natural language processing to automatically create sample code, data sets and even unit tests. By doing this, the tool is streamlining the development process, enabling developers to focus more on high-level tasks rather than the nitty-gritty of code writing.

Leveraging Data Integration Platforms

Generative AI technologies have also improved data integration capabilities by using historical data, analyses of trends, customer behaviors and market dynamics. This advancement is particularly influential in the finance, retail and healthcare sectors, where predictive insights are critical for strategic and operational decisions. There's been a shift towards adopting data lake house architectures, which combine the features of data lakes and data warehouses to help meet the challenges of handling large, varied data types and formats, providing both scalability and efficient management. This evolution in data architecture caters to the growing complexity and volume of data in various industries.

Integrating various data sources is crucial for many companies to enhance their business operations. Software AG has introduced Super iPaaS, an evolution of the traditional integration platform as a service (iPaaS). This advanced platform is AI-enabled and designed to integrate hybrid environments, offering expansive integration capabilities. Cloudera has also made strides with new data management features that incorporate generative AI, enabling the use of unstructured data both on-premises and in cloud environments. Its hybrid approach effectively consolidates client data for better management. Informatica's intelligent data management cloud platform integrates AI and automation tools, streamlining the process of collecting, integrating, cleaning and analyzing data from diverse sources and formats. This creates an accessible data repository that benefits business intelligence and analytics.

That’s a Wrap!

In my collaborations throughout the year with various companies, one key theme has emerged in this AI-driven era – data has become even more fundamentally important for businesses. It's clear that the success of AI heavily relies on the quality of the data it uses, and AI models are effective only when the data they process is accurate, relevant and unbiased.

For example, in applications such as CRM or supply chain optimization, outcomes are directly influenced by the data’s integrity. Instances where AI failed to meet expectations could often be traced to poor data quality, whether it was incomplete, outdated or biased. This year has highlighted the necessity of not just collecting large amounts of data but ensuring its quality and relevance. Real-world experience underscores the need for strict data governance and the implementation of systems that guarantee data accuracy and fairness, all of which are essential for the effective use of AI in business.

As AI technology advances and data quality improves, the use of generative AI in understanding and engaging with customers is becoming ever more prominent. Backed by good data management, this enhances the customer experience by making the customer journey more personalized and informative. It allows businesses to gain valuable insights from customer interactions, helping them continuously refine and Excellerate their offerings and customer relations. I expect this trend to grow, further emphasizing the role of AI in customer engagement and shaping business strategies. In fact, this symbiotic relationship between AI-driven personalization and customer engagement is becoming a cornerstone of not only data management strategy but modern business strategy overall, significantly impacting how companies connect with their customers.

Wrapping up, it's evident that the emphasis on data quality is critical for improving AI's performance. Data management, cloud services, data protection and governance, databases, data integration and intelligent platforms have all significantly contributed to the advancement of AI. In 2024, I expect we’ll see even more emphasis on ensuring the accuracy and relevance of data so that AI can provide dependable insights.

Sun, 31 Dec 2023 09:37:00 -0600 Robert Kramer en text/html https://www.forbes.com/sites/moorinsights/2023/12/31/2023-the-year-generative-ai-transformed-enterprise-data-management/
Why training LLMs with endpoint data will strengthen cybersecurity

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Capturing weak signals across endpoints and predicting potential intrusion attempt patterns is a perfect challenge for Large Language Models (LLMs) to take on. The goal is to mine attack data to find new threat patterns and correlations while fine-tuning LLMs and models.

Leading endpoint detection and response (EDR) and extended detection and response (XDR) vendors are taking on the challenge. Nikesh Arora, Palo Alto Networks chairman and CEO, said, “We collect the most amount of endpoint data in the industry from our XDR. We collect almost 200 megabytes per endpoint, which is, in many cases, 10 to 20 times more than most of the industry participants. Why do you do that? Because we take that raw data and cross-correlate or enhance most of our firewalls, we apply attack surface management with applied automation using XDR.”  

CrowdStrike co-founder and CEO George Kurtz told the keynote audience at the company’s annual Fal.Con event last year, “One of the areas that we’ve really pioneered is that we can take weak signals from across different endpoints. And we can link these together to find novel detections. We’re now extending that to our third-party partners so that we can look at other weak signals across not only endpoints but across domains and come up with a novel detection.” 

XDR has proven successful in delivering less noise and better signals. Leading XDR platform providers include Broadcom, Cisco, CrowdStrike, Fortinet, Microsoft, Palo Alto Networks, SentinelOne, Sophos, TEHTRIS, Trend Micro and VMWare.

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Why LLMs are the new DNA of endpoint security 

Enhancing LLMs with telemetry and human-annotated data defines the future of endpoint security. In Gartner’s latest Hype Cycle for Endpoint Security, the authors write, “Endpoint security innovations focus on faster, automated detection and prevention, and remediation of threats, powering integrated, extended detection and response (XDR) to correlate data points and telemetry from endpoint, network, web, email and identity solutions.”

Spending on EDR and XDR is growing faster than the broader information security and risk management market. That’s creating higher levels of competitive intensity across EDR and XDR vendors. Gartner predicts the endpoint protection platform market will grow from $14.45 billion today to $26.95 billion in 2027, achieving a compound annual growth rate (CAGR) of 16.8%. The worldwide information security and risk management market is predicted to grow from $164 billion in 2022 to $287 billion in 2027, achieving an 11% CAGR.  

CrowdStrikes’ CTO on how LLMs will strengthen cybersecurity 

VentureBeat recently sat down (virtually) with Elia Zaitsev, CTO of CrowdStrike to understand why training LLMs with endpoint data will strengthen cybersecurity. His insights also reflect how quickly LLMs are becoming the new DNA of endpoint security.

VentureBeat: What’s the catalyst to drove you to start looking at endpoint telemetry data as a source of insight that could eventually be used to train LLMs? 

Elia Zaitsev: “So when the company was started, one of the reasons why it was created as a cloud-native company is that we wanted to use AI and ML technologies to solve tough customer problems. Because if you think about the legacy technologies, everything was happening at the edge, right? You were making all the decisions and all the data lived at the edge, but there was this idea we had that if you wanted to use AI technology, you needed to have, especially for those older ML type solutions, which are still by the way, very effective. You need that quantity of information and you can only get that with a cloud technology where you can bring in all the information. 

We could train these heavy-duty classifiers into the cloud and then we can deploy them at the edge. So train in the cloud, deploy to the edge, and make smart decisions. The funny thing though, is that’s occurring now that generative AI is coming into the fore and they’re different technologies. Those are less about deciding what’s good and what’s bad and more about empowering human beings like taking a workflow and accelerating it.”

VentureBeat: What’s your perspective on LLMs and gen AI tools replacing cybersecurity professionals? 

Zaitsev: “It’s not about replacing human beings, it’s about augmenting humans. It’s that AI-assisted human, which I think is such a key concept, and I think too many people in technology, and I’ll say this as a CTO, I’m supposed to be all about the technology the focus sometimes goes too far on wanting to replace the humans. I think that’s very misguided, especially in cyber. But when you think about the way the underlying technology works, gen AI, it’s actually not necessarily about quantity. Quality becomes much more important. You need a lot of data to create these models to begin with, but then when it comes time to actually teach it to do something specific, and this is key when you want to go from that general model that can speak English or whatever language, and you want to do what’s called fine-tuning when you want to teach it, how to do something like summarize an incident for a security analyst or operate a platform, these are the kinds of things that our generative product Charlotte AI is doing.”

VentureBeat: Can you discuss how automation technologies like LLM affect the role of humans in cybersecurity, especially in the context of AI usage by adversaries and the ongoing arms race in cyber threats?

Zaitsev: “Most of these automation technologies, whether it’s LLMs or something like that, they don’t tend to replace humans really. They tend to automate the rote basic tasks and allow the expert humans to take their valuable time and focus on something harder. Usually, people start asking, what about the adversaries using AI? And to me it’s a pretty simple conversation. In a typical arms race, the adversaries are going to use AI and other technologies to automate some baseline level of threats. Great. You use AI to counteract that. So you balance that out and then what do you have left? You’ve still got a really savvy, smart human attacker rising above the noise, and that’s why you’re still going to need a really smart, savvy defender.”

VentureBeat: What are the most valuable lessons you’ve learned using telemetry data to train LLMs? 

Zaitsev: “When we build LLMs, it’s actually easier to train many small LLMs on these specific use cases. So take that Overwatch dataset, that Falcon Complete data, that [threat] intel dataset. It’s actually easier and less prone to hallucination to take a small purpose-built large language model or maybe call it a small language model if you will. 

You can actually tune them and get higher accuracy and less hallucinations if you’re working on a smaller purpose-built one than trying to take these big monolithic ones and make them like a jack of all trades. So what we use is a concept called a mixture of experts. You actually in many cases get better efficacy with these LLM technologies when you’ve got specialization, right? A couple of really purpose-built LLMs working together versus trying to get one super smart one that actually doesn’t do anything particularly well. It does a lot of things poorly versus any one thing particularly well.

We also apply validation. We’ll let the LLMs do some things, but then we’ll also check the output. We’ll use it to operate the platform. We’re ultimately basing the responses on our telemetry on our platform API so that there’s some trust in the underlying data. It’s not just coming out of the ether, out of the LLMs brain, so to speak, right? It’s rooted in a foundation of truth. 

VentureBeat: Can you elaborate on the importance and role of expert human teams in the development and training of AI systems, especially in the context of your company’s long-term approach towards AI-assisted, rather than AI-replaced, human tasks?”

Zaitsev: When you start to do those types of use cases, you don’t need millions and billions and trillions of examples. What you need is actually in many cases, a couple of thousand, maybe tens of thousands of examples, but needed to be very high quality and ideally what we call human-annotated data sets. You basically want an expert to say to the AI systems, this is how I would do it, learn from my example. So I won’t take credit and say we knew that the generative AI boom was going to happen 11, 12 years ago, but because we were always passionate believers in this idea of AI assisting humans not replacing humans, we set up all these expert human teams from day one.

So as it turns out, because we’ve in many ways uniquely been investing in our human capacity and building up this high-quality human annotated platform data, we now all of a sudden have this goldmine, right, this treasure trove of exactly the right kind of information you need to create these generative AI large language models, specifically fine-tuned to cybersecurity use cases on our platform. So a little bit of good luck there.

VentureBeat: How are the advances you’re making with training LLMs paying off for current and future products?  

Zaitsev:  Our approach, I’ll use the old adage when all you have is a hammer, everything looks like a nail, right? And this is not true just for AI technology. It is the way we approach data storage layers. We’ve always been a fan of this concept of using all the technologies because when you don’t constrain yourself to use one thing, you don’t have to. So Charlotte is a multi-modal system. It uses multiple LLMs, but it also uses non-LLM technology. LLMs are good at instruction following. They’re going to take a natural language interfaces and convert them into structured tasks.

VentureBeat: Are your LLMs training on customer or vulnerability data? 

Zaitsev: The output that the user sees from Charlotte is almost always based off of some platform data. For example, vulnerability information from our Spotlight product. We may take that data and then tell Charlotte to summarize it for a layperson. Again, things that LLMs are good at, and we may train it off of our internal data. That’s not customer-specific, by the way. It’s general information about vulnerabilities, and that’s how we deal with the privacy aspects. The customer-specific data is not training into Charlotte, it’s the general knowledge of vulnerabilities. The customer-specific data is powered by the platform. So that’s how we keep that separation of church and state, so to speak. The private data is on the Falcon platform. The LLMs get trained on and hold general cybersecurity knowledge, and in any case, make sure you’re never exposing that naked LLM to the end user so that we can apply the validation. 

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Fri, 29 Dec 2023 17:13:00 -0600 Louis Columbus en-US text/html https://venturebeat.com/security/why-training-llms-with-endpoint-data-will-strengthen-cybersecurity/
The Top Ten HR Trends That Matter Most In 2024

Rapid use of Generative AI, the emergence of a new blended workforce of humans and digital workers, and increasing employee fear of being obsolete (FOBO), together prove what Intel co-founder Gordon Moore (and the author of Moore’s Law) said, at any given point in time, “change has never been this fast and will never be this slow ever again.”

Resiliency, agility, and adopting a “test and learn approach” will mark the winning strategies for 2024.

As I have done in in 2016, 2017, 2018, 2019, 2020, 2021, 2022, and 2023, here is my countdown of what you should include on your HR roadmap for 2024.

1. Generative AI Will Be Your New Work Buddy

In 2024, Generative AI will shake up how we think about what work we do and what we delegate to our new “work buddy.” This will be the year of specialized AI powered assistants working side by side with humans. Teachers will get a copilot for homework grading, such as Class Companion. Architects will get an AI assistant for design, SketchPro, and online learners studying at Kahn Academy will be able to access Kahnmigo to aid in brainstorming helping them to become better writers and learners while decreasing a teacher’s administrative workload.

Walmart launched one of the largest AI powered work buddies, called My Assistant, an AI powered app to help Walmart’s 50,000 corporate employees be more productive in summarizing long documents, creating new content, and taking over routine tasks that humans did in the past. Microsoft’s Co-Pilot, soon to launch, will integrate generative AI into Microsoft 365, allowing workers to perform at a much higher level. Jared Spataro, Microsoft Corporate Vice President, believes Microsoft Co-Pilot will transform meetings from a point in time and place to a knowledge object. This means employees working with Microsoft Co-Pilot will not only be able to create a summary of the meeting, but also use Co-Pilot to query the group’s sentiment on the meeting topic, the dissenting opinions, who the key dissenters were, and the agreed-to next steps. While these AI powered work buddies can boost efficiency and performance, it will be up to humans to have a strategy to capitalize on their potential.

2. Generative AI Will Impact How Managers Lead

Generative AI has evolved to one of the fastest adopted technologies, with nearly 200 million users of ChatGPT since it launched on November 30, 2022. Business and HR leaders are struggling with how to keep pace with rapid adoption in the workplace. The impact on managers is not simply in training employees on how to use Generative AI, but also in helping managers lead teams of humans and digital workers. Morgan Stanley estimates Generative AI technologies will likely affect a quarter of all occupations that exist today, and this will rise to 44% within three years. To address this, companies and education providers need to provide training on how to use Generative AI. Within the next three years, this will translate into a $16 billion market for re-skilling workers displaced by Generative AI.

As Generative AI becomes increasingly adept at problem solving, it will be up to managers to get better at problem finding. Managers will be working side-by-side with their human and digital teams to foster a culture of continuous learning while leaning into their uniquely human skills, such as relationship building, communications, and collaboration. One thing is certain: Generative AI will drive organizational change, impact workflows, automate some jobs, and create new ones. But it will always be humans augmented by machines that will create innovation.

3. The Fear of Obsolescence Will Force Companies to Increase Spending in Career Development & Mobility

While employers see the promise of increased productivity using Generative AI, a exact survey by EY reports that 75% of employees are concerned AI will make certain jobs obsolete, and two-thirds say they fear AI will replace their job. This fear of being obsolete is causing employees to seek out training in using Generative AI and acquiring new skills to better deal with their fears. PwC announced a $1 billion investment in training its workers on AI with courses on syllabus such as the ethics of AI, responsible use of AI, and how to create AI prompts to generate the best results. Their goal is to engage 75,000 U.S. PwC employees to both enroll and complete these courses.

In addition to training employees on how to use Generative AI, there is an explosion of new internal talent marketplaces as companies continue to find it difficult to successfully recruit external talent. Internal talent marketplaces have become the de facto way companies connect employees with internal career opportunities and resources to grow in their careers. For example, Grow at Key is KeyBank’s AI powered employee-led, and manager supported talent platform to support internal talent mobility. Launched with the vision that every employee can be the CEO of her own career, Grow at Key provides a range of resources, from matching employees to new job roles to accessing mentoring, coaching, and stretch assignments. To date, there has been a 60% increase in employees engaging in training programs and one in three KeyBank employees have enrolled in an Investing in My Development session to prepare them for understanding how to better manage their careers.

4. Hybrid Work Environments Are Good for Business

Companies need to stop debating the merits of hybrid work and realize this is the new way of working for many knowledge workers as we enter 2024. An ADP survey of 32,000 workers reports that 64% of workers would consider quitting if asked to return to the office full time. Recent research shows companies that allow choice and a remote first/hybrid work environment have revenue growth four times faster than their peers who are more stringent about office attendance. This research conducted by Boston Consulting Group and Scoop Technologies Inc. among 554 public companies employing 26.7 million people, found that “fully flexible” firms — which are either completely remote or allow employees to choose when they come to an office — increased sales 21% between 2020 and 2022, on an industry-adjusted basis. The better growth rates for more remote-friendly companies reflects their ability to hire faster and from a wider geographic area, along with higher rates of employee retention.

As hybrid work becomes the norm, leaders must have a plan to combat proximity bias, or the phenomenon of favoring in-person workers for career development, mentoring, and stretch assignments at the expense of those who work remotely. Hybrid work environments “work” when leaders ensure they are equitable for all and grant autonomy for individual leaders to determine when and where work happens, rather than follow a CEO mandate. Leaders must remember presence in the office does not equate to performance, so they need to shift from “managing by walking around,” to “managing by connecting across geographies.”

5. The Workforce Is a Blend of Full-time Employees, Part-Timers, Contingent Workers, and Digital Workers

Remember when the blended workforce was simply a distinction between full-time workers and part-time workers. Today’s work now gets accomplished by a blend of full-time workers, part-timers, teams of contractors, contingent workers, and digital workers, the later designed to augment some tasks of human labor. In fact, Statista reports a growth in all sectors except full-time workers, with part-timers (growing from 20 million in 1990 to 26 million in 2022) and contingent workers forecast to be half of the US workforce by 2027.

This blended workforce is not a new concept; it’s been around for decades. What’s different now is that a greater percentage of key jobs are performed by contingent workers. Overall, we are starting to see upwards of 30% to 50% of a global organization’s total workforce composed of contingent workers. MIT Sloan Management Review reported that the Novartis workforce includes 110,000 full time employees plus 50,000 contractors and temporary workers. Cisco has 83,000 full time employees and 50,000 plus contingent workers of various types. This new workforce ecosystem requires a new set of management practices, and leadership approaches, especially as 81% of companies in a exact HBR Analytical Services survey report that contingent workers are important to their organization, but only 38% say that their organization is effective at managing them.

Leaders will need to acknowledge they will have more types of contributors—human and digital, full time, part time and contingent—that must seamlessly work together. What is needed is a total workforce strategy where HR plays a central role in coordinating all the cross-functional disciplines that hire internal and external workers.

6. The Four Day Work Week is Desired by Both Front-line and Knowledge Workers

Many of the assumptions we have about how we work, when we work, the needs of our workers, the demographics of the workforce, and where we work have been changed forever. One of the major changes, as we move into 2024, is our assumptions about a five-day work week. All workers, including both frontline and knowledge workers, want flexibility in when they work. Research conducted among 1,301 workers found 41% of both front-line workers and knowledge workers want flexibility in when they work, and 56% of front-line workers and 69% of knowledge workers want the ability to opt for a 4-day work week with no pay reduction.

As I wrote in my Forbes column, the UK four-day work week pilot was conducted among 61 companies and found 56% intend to continue trying the four-day week citing benefits of increased productivity and decreased employee attrition. These UK companies adopted the 80-100-100 model of flexible working: a drop in hours to 80% of their standard work week, while retaining 100% pay and 100% productivity.

Experiments on a shortened work week are also occurring in the US. For example, several Chick-Fil-A stores are allowing front-line workers the opportunity to work 13 hour shifts for three consecutive days with full pay. The results so far have been increased retention while maintaining customer service efficiencies.

However, it’s important to remember the three- or four-day work week is not a one size fits all solution. Companies should adopt a “test and learn approach,” focusing on the type and magnitude of changes required to allow for a shortened work week, the level of employee and manager training needed, and the type of well-being support required for new ways of working.

7. Cognitive Skills Will Increase in Importance as Generative AI Gains Traction

The World Economic Forum estimates that 44% of a workers’ core skills are expected to change in the next five years. While there are endless lists of the most important skills for the future, World Economic Forum identifies five core skills that will increase in importance in the next five years. This are shown in Figure 1 as:

Cognitive skills are growing in importance, reflecting the increased demand for creative and analytical thinking in the age of AI. This combined with technological literacy, the third fastest growing skill along with the skills of resilience, flexibility, curiosity, and lifelong learning are evidence that leaders will continue to emphasize a culture of lifelong learning. As Generative AI gains traction, one skill I see that is foundational to all of them is the ability to be digitally curious, meaning seeking out and using new and emerging digital technologies to enhance one’s cognitive skills. I think about the past few years where some of the new technologies I have used include Mural, an online visual collaborative tool, StreamYard, a live streaming studio, and ChatGPT 4.0 and Pictory, to create a new video entitled, Flexible Work for All. This is the start of building my digital curiosity muscle, trying out these platforms and seeing what they might mean for enhancing my work.

8. Holistic Financial Well-being Is The Must Have Benefit for 2024

Financial well-being among workers is eroding. LendingClub reports 61% of American workers live paycheck to paycheck and lack a core set of financial literacy skills, and over half of workers earning $100,000 are living paycheck to paycheck.

Unifi, a leader in aviation services with more than 23,000 employees, became aware of this two years ago and created a new offering for frontline workers to access their earned wages on-demand. Since then, this has grown beyond early access to wages to a holistic financial well-being offering with almost 20% frontline workforce enrolled and taking advantage of free services including anonymous financial coaching and customizable saving plans. Dr. Archana Arcot, Chief People Officer at Unifi, believes that, “the major driver of this program is to relieve an employee’s financial stress and make financial wellness part of their everyday life.”

MetLife reports nearly half of workers surveyed cited financial concerns as the cause of poorer mental health. To address this, Unifi adopted a Goal Based Savings program which resulted in the company moving to the top 1% with a total amount saved of $10,000+ YTD, as reported by Payactiv. By offering holistic financial well-being programs, companies like Unifi can not only enhance their employee experience but build financial wellness into their employer value proposition and be better able to attract talent in a tight labor market.

9. The Sexy C-Suite Job For 2024 Is Chief Artificial Intelligence Officer

While we have seen a myriad of new C-suite job roles in the past decade—from Chief Medical Officer to Chief Ethics Officer—the Chief Artificial Intelligence Officer is coming at a time when organizations are looking for guidance with how to create guidelines and policies for safe and ethical use of generative AI in the workplace. LinkedIn reports 44% globally and 57% in the U.S. say their organizations don’t have policy guidelines or training for how to use these new tools at work.

Organizations are starting to appoint a new C-suite player to lead this effort. Research from Foundry, an IDG company, finds 11% of mid-size to large organizations already have an individual with the role of Chief Artificial Intelligence Officer and another 21% of organizations are actively seeking such a person for this role. This role is growing in importance as business leaders develop an AI strategy, create governance practices, and engage cross functional leaders in safe, ethical, and responsible use of generative AI.

A new Wavestone survey of Data and AI leaders found 61.7% report the responsibility for Generative AI is currently part of the Chief Data Officer (CDO) remit. But a growing number of organizations are creating a new role, the Chief Artificial Intelligence Officer (CAIO) to oversee AI developments for their organization. “This will be very much a focus during 2024,” says Randy Bean, Innovation Fellow at Wavestone.

10. An Organization’s Sustainability Record Will Attract and Retain Talent

As the talent marketplace continues to be competitive, climate change and sustainability has become one of the defining challenges for current and future generations. A growing number of publicly traded companies, such as Alphabet, Apple, Cisco, and PayPal, have created sustainability annual reports with Alphabet committing its entire $5.57B Sustainability Bond to support environmentally and socially responsible projects such as clean energy, clean transportation, and circular economy design.

A company’s sustainability record is proving to make a difference in talent acquisition and retention. Research by IBM Institute for Business Value, found 70% of workers and those in the job market, are drawn to environmentally sustainable employers. And almost half of these workers said they would take a lower salary to work for environmentally and socially responsible organizations. Gartner predicts employers will respond by promoting climate change protections, such as offering employees shelter during natural disasters, as part of employee benefit offerings.

In addition, a growing number of universities are building ambitious goals to integrate sustainability into their curriculum, campus operations, and endowment. The University of Toronto, placing first out of 1,400 universities in environmental and social impact, is making headway to de-carbonize its campus by 2050, developing new energy efficient student centers, and committing to climate responsible construction.

The Global Business School Network, a network of 150 global business schools in 50 countries, has several new initiatives for universities to share sustainability “next practices.” While companies and universities are each addressing sustainability, I see the need for greater collaboration among them, as both employees and students weigh the environmental impact of their employer and university. Sustainability is both a business and educational issue requiring joint corporate and university solutions.

What’s important to you as you reflect on your 2024 HR Playbook?

Follow me on Twitter and LinkedIn.

Thu, 04 Jan 2024 05:12:00 -0600 Jeanne Meister en text/html https://www.forbes.com/sites/jeannemeister/2024/01/04/the-top-ten-hr-trends-that-matter-most-in-2024/
Cisco: Losing Ground On Both The Networking And Security Fronts
CISCO headquarters in Silicon Valley

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Cisco (CSCO) has exhibited a modest 3.1% average revenue growth and 4.8% average adjusted operating profit growth over the past five years. The company is experiencing a decline in market share within both the networking and security end markets. Consequently, I

Tue, 26 Dec 2023 03:18:00 -0600 en text/html https://seekingalpha.com/article/4659734-cisco-stock-losing-ground-both-networking-and-security-fronts-sell
Top 13 Cybersecurity Companies in the USA in 2024

With new threats emerging and existing ones becoming more sophisticated, cybersecurity is no longer a quiet backroom concern. Failure to prioritize cybersecurity leaves businesses, governments, and individuals vulnerable to crippling attacks with far-reaching consequences. To stay ahead of the curve, you need to partner with the right companies that are innovating and pushing the boundaries of security. That’s why we have created a list of top cybersecurity companies in the USA to watch in 2024.  

The Cost of Neglect:

Cybersecurity breaches are no longer just an inconvenience. They’re financial sinkholes:

  • The average cost of a data breach in 2023 is $4.45 million.
  • Ransomware attacks spiked by over 37% in 2023, with the average enterprise ransom exceeding $100,000. Some even faced jaw-dropping demands of $5.3 million.
  • Email-based phishing attacks have surged a whopping 464% in the first half of 2023. 

Finding the Right Partner Matters:

From ransomware to nation-state espionage, the threats are diverse. Your partner needs the experience to anticipate every attack and the agility to deploy countermeasures on the fly. The right cybersecurity firms in the USA bring:

  • Expertise: Deep understanding of the threat landscape, vulnerabilities, and evolving tactics.
  • Technology: Cutting-edge solutions for endpoint protection, network security, threat intelligence, and incident response.
  • Proactive Approach: Continuous monitoring, analysis, and adaptation to stay ahead of the curve.
  • Scalability: Solutions tailored to your specific needs and growth. 

Now, without further ado, here are the top cybersecurity companies in the USA to watch in 2024.

  1. Strobes
  2. Palo Alto Networks
  3. WeSecureApp
  4. Microsoft
  5. Fortinet
  6. Cisco
  7. IBM Security
  8. Okta
  9. Sophos
  10. SentinelOne
  11. Crowdstrike
  12. Tenable
  13. Rapid7

1. Strobes

Company Overview: Founded in 2019, Strobes is a rapidly growing cybersecurity innovator, empowering organizations of all sizes with cutting-edge security solutions. We prioritize flexibility, tailoring the expertise to meet the unique needs of enterprises, SMBs, and across various industries.

Specialties: Strobes shines in its mastery of cutting-edge technologies. Their flagship offerings include:

Key Differentiators: Strobes stands apart through its:

  • Fusion of automation and human expertise: Their solutions combine powerful AI-driven tools with the critical insights of experienced security professionals.
  • Client-centric approach: Strobes collaborates closely with clients to understand their unique needs and tailor solutions that fit their specific risk landscape.
  • Focus on continuous improvement: Constant innovation and adaptation to the ever-changing threat landscape are hallmarks of Strobes philosophy.

Target Audience: Strobes caters to organizations of all sizes with complex IT infrastructures, particularly those operating in high-risk industries. Their solutions are ideal for organizations seeking proactive, data-driven security strategies.

2. Palo Alto Networks

Company Overview: Palo Alto Networks is a cybersecurity behemoth, established in 2005, renowned for its Next-Generation Firewalls (NGFWs) and comprehensive security suite. They cater primarily to large enterprises and government agencies.

Specialties: Palo Alto Networks’ core strengths lie in:

  • Next-Generation Firewalls: Their NGFWs offer advanced threat detection and prevention capabilities, including deep packet inspection and application identification.
  • Cloud Security: They provide cloud-native security solutions for protecting workloads across public, private, and hybrid cloud environments.
  • Endpoint Security: Their endpoint protection solutions offer a comprehensive defense against malware, ransomware, and other targeted attacks.

Key Differentiators: Palo Alto Networks stands out through its:

  • Established Brand: Their large market share and established reputation make them a trusted choice for large organizations.
  • Broad Product Portfolio: They offer a wide range of security solutions, catering to a diverse set of security needs.
  • Proven Track Record: They have a long history of success in protecting large enterprises from sophisticated cyberattacks.

Target Audience: Palo Alto Networks primarily targets large enterprises and government agencies with complex security needs and substantial budgets.

3. WeSecureApp

Company Overview: WeSecureApp is a rapidly growing Indian cybersecurity company, founded in 2013, specializing in offensive security solutions including, application, cloud, and network penetration testing. WeSecureApp’s agility and focus on developer experience set them apart. Their cloud-based platform makes security accessible and easy to implement, even for organizations with limited security resources. Their unique blend of automated tools and human expertise provides a comprehensive and cost-effective approach to application security. 

Specialties: WeSecureApp’s core strengths lie in:

  • Application Security: They offer comprehensive security assessments for web and mobile applications, identifying vulnerabilities and recommending remediation strategies.
  • Penetration Testing: Their skilled ethical hackers perform in-depth penetration tests to uncover security weaknesses and simulate real-world attacks.
  • Cloud Security: They provide expertise in securing cloud environments, including infrastructure, applications, and data.

Key Differentiators: WeSecureApp stands out through their:

  • Focus on Application Security: They have a deep understanding of web and mobile application security, making them a valuable partner for organizations with digital assets.
  • Cost-Effective Solutions: They offer competitive pricing, making their services accessible to SMBs and mid-sized businesses with limited budgets.

Target Audience: Organizations of all sizes, particularly those developing and deploying web and mobile applications, who value personalized service and a consultative approach to application security.

4. Microsoft

Company Overview: A global tech behemoth, Microsoft has leveraged its vast resources to become a cybersecurity powerhouse. With over 220,000 employees and a focus on cloud-based solutions, Microsoft caters primarily to enterprise clients across various industries.

Specialties: Microsoft boasts a diverse suite of offerings, including Azure Sentinel (SIEM), Defender for Endpoint (EDR), and Azure Active Directory (identity management). Their strength lies in cloud-native security architecture, scalability, and integration with other Microsoft products.

Key Differentiators: Microsoft excels in leveraging its extensive cloud infrastructure and AI capabilities to deliver proactive threat detection and response. Additionally, their deep integration with existing Microsoft ecosystems provides seamless security for organizations already invested in the platform.

Target Audience: Enterprise organizations looking for a comprehensive, cloud-based security solution with seamless integration into existing Microsoft environments.

5. Fortinet

Company Overview: Founded in 2006, Fortinet has rapidly climbed the cybersecurity ladder, becoming a major player with over 12,000 employees. Their focus lies on integrated security appliances and platforms, catering to a broad range of clients from SMBs to large enterprises.

Specialties: Fortinet’s forte lies in its Security Fabric, a unified platform encompassing firewalls, intrusion prevention, sandboxing, and endpoint protection. They prioritize integrated, all-in-one solutions with simplified management and visibility.

Key Differentiators: Fortinet’s commitment to an integrated approach, coupled with its broad product portfolio and competitive pricing, sets it apart. They offer a compelling option for organizations seeking a one-stop shop for their security needs.

Target Audience: SMBs and enterprises seeking an integrated security platform with simplified management and a cost-effective approach.

6. Cisco

Company Overview: A networking giant with a rich history, Cisco has expanded its reach into cybersecurity through strategic acquisitions and organic development. With over 80,000 employees, they cater to a wide range of clients across all industries.

Specialties: Cisco excels in network security solutions, including firewalls, intrusion detection and prevention systems, and secure access control. They emphasize visibility and control over network traffic, along with integration with other Cisco networking products.

Key Differentiators: Cisco’s strength lies in its extensive network expertise and ability to provide comprehensive network-based security solutions. Their focus on integration and collaboration within existing network environments makes them a valuable partner for organizations heavily invested in Cisco technology.

Target Audience: Enterprises with complex network infrastructures seeking robust network security solutions and seamless integration with existing Cisco products.

 7. IBM Security

Company Overview: A tech giant since 1911, IBM Security boasts a rich history of innovation, acquiring numerous security firms over the years to build a comprehensive portfolio. Their size and global reach make them a dominant force in enterprise security, catering to large organizations across various industries.

Specialties: IBM Security excels in X-Force threat intelligence, security information and event management (SIEM), and cloud security solutions. Their QRadar SIEM platform is a popular choice for large enterprises, while their cloud security offerings integrate seamlessly with major cloud providers.

Key Differentiators: IBM’s sheer size and resources allow them to invest heavily in research and development, constantly pushing the boundaries of security technology. Their focus on AI and machine learning-powered solutions helps them stay ahead of the curve in threat detection and prevention.

Target Audience: Primarily large enterprises and government agencies with complex security needs and budgets to match.

8. Okta

Company Overview: Founded in 2009, Okta is a relative newcomer compared to IBM but has quickly become a leader in identity and access management (IAM). Their focus on cloud-based solutions and user-friendly interfaces has made them a favorite among businesses of all sizes.

Specialties: Okta’s core strength lies in its comprehensive IAM platform, which simplifies user authentication and access control across various applications and cloud services. Their single sign-on (SSO) technology eliminates the need for juggling multiple logins, improving both security and user experience.

Key Differentiators: Okta’s cloud-native approach and API-driven architecture make it highly scalable and adaptable to diverse IT environments. Their focus on user experience and ease of integration has earned them a loyal following among businesses seeking a streamlined approach to identity management.

Target Audience: Businesses of all sizes, particularly those with cloud-based applications and a focus on user-centric security.

9. Sophos

Company Overview: Founded in 1983, Sophos is a veteran in the cybersecurity space, initially known for its antivirus software. Today, they offer a wide range of security solutions for businesses of all sizes, with a strong focus on endpoint protection and synchronized security.

Specialties: Sophos’s core strength lies in its synchronized security approach, which combines endpoint protection, network security, and cloud security into a single platform. This integrated approach simplifies security management and provides comprehensive protection against modern threats.

Key Differentiators: Sophos’s focus on synchronized security and ease of use makes them a popular choice for small and medium-sized businesses (SMBs) seeking an all-in-one security solution. Their competitive pricing and flexible deployment options further enhance their appeal.

Target Audience: SMBs and enterprises seeking a comprehensive and affordable security solution with a focus on ease of use and management.

10. SentinelOne

Company Overview: Founded in 2013, SentinelOne is a rising star in the cybersecurity world, known for its innovative approach to endpoint protection. Their focus on behavioral analysis and machine learning helps them detect and prevent even the most sophisticated cyberattacks.

Specialties: SentinelOne’s core strength lies in its Singularity XDR platform, which combines endpoint protection, endpoint detection and response (EDR), and incident response capabilities into a single solution. This unified approach provides real-time threat visibility and rapid response to security incidents.

Key Differentiators: SentinelOne’s use of machine learning and behavioral analysis makes it highly effective in detecting and preventing zero-day attacks and advanced threats. Their cloud-based architecture and proactive approach to security make them a compelling choice for organizations looking for cutting-edge endpoint protection.

Target Audience: Enterprises and organizations with high-value assets and a need for advanced endpoint protection against sophisticated cyberattacks.

11. CrowdStrike

Company Overview: Founded in 2011, CrowdStrike has become a cybersecurity titan, powering endpoint protection and threat intelligence for enterprises across diverse industries. Their global reach and proven track record make them a force to be reckoned with.

Specialties: CrowdStrike’s bread and butter lies in endpoint security (EDR, XDR), incident response, and threat intelligence. Their Falcon platform provides comprehensive endpoint visibility and proactive threat hunting, making them a popular choice for large-scale organizations.

Key Differentiators: CrowdStrike boasts an impressive cloud-native architecture, a global threat intelligence network, and robust incident response capabilities. Their aggressive threat-hunting approach and focus on real-time detection resonate with organizations facing high-stakes security challenges.

Target Audience: Their solutions cater primarily to large enterprises with complex IT environments and demanding security needs. Their expertise in incident response and threat intelligence makes them ideal for organizations navigating high-risk landscapes.

12. Tenable

Company Overview: Established in 2004, Tenable has carved a niche in the vulnerability management space. Their focus on continuous vulnerability assessment and prioritization makes them a go-to choice for organizations seeking proactive threat mitigation.

Specialties: Tenable’s forte lies in vulnerability management, compliance, and attack surface management. Their Nessus platform offers comprehensive scans, prioritized remediation recommendations, and real-time vulnerability tracking, ensuring organizations stay ahead of potential security vulnerabilities. 

Key Differentiators: They focus on vulnerability prioritization and integration with security orchestration and response (SOAR) platforms sets them apart. Their ability to streamline vulnerability management processes and provide actionable insights makes them a valuable asset for organizations seeking to optimize their security posture.

Target Audience: Tenable solutions cater to organizations of all sizes, particularly those with complex IT infrastructures and a need for robust vulnerability management strategies. Their scalable solutions and industry-specific compliance tools make them a versatile option for diverse security needs.

13. Rapid7

Company Overview: Founded in 2000, Rapid7 has carved a path in the realm of security information and event management (SIEM) and vulnerability management. Their focus on security analytics and automation makes them a popular choice for organizations seeking to optimize their security operations.

Specialties: Their core strengths lie in SIEM, incident response, and vulnerability management. Their Insight platform provides real-time threat detection, log management, and incident response capabilities, enabling organizations to gain insights and respond to threats effectively.

Key Differentiators: Rapid7’s cloud-based SIEM solution and focus on automation through SOAR integrations set them apart. Their ability to leverage security data for actionable insights and automate security workflows makes them a valuable asset for organizations seeking to streamline their security operations.

Target Audience: Rapid7’s solutions cater to organizations of all sizes, particularly those seeking to Excellerate their security analytics and automation capabilities. Their user-friendly platform and broad range of security tools make them a versatile option for diverse security needs.

Company Name Overview Founded in 2019, dynamic growth, and global reach. Differentiators Target Audience
Strobes  Established brand and broad product portfolio. ASM, PTaaS, RBVM, CTEM expertise. Fusion of automation and human expertise, Client-centric approach SMBs and enterprises of all sizes.
Palo Alto Networks Established in 2005, cybersecurity giant.  NGFWs, Cloud Security, Endpoint Security.  The integrated approach is a broad product portfolio. Large enterprises, government agencies
WeSecureApp Founded in 2013, agile cybersecurity company. Application Security, Penetration Testing.  Focus on application security, Cost-effective solutions Organizations of all sizes, app development.
Microsoft Global tech behemoth, cloud focus.  Cloud-based solutions, AI capabilities. Cloud-based solutions, Extensive cloud infrastructure. Enterprise organizations with complex IT environments. 
Fortinet Founded in 2006, integrated security.   Security Fabric, Integrated solutions.  Extensive network expertise and comprehensive network-based solutions. SMBs and enterprises.
CISCO Networking giant, extensive reach.  Network security, Firewalls, Access control. Large enterprises, and government agencies. Enterprises with complex network infrastructures.
IBM Tech giant since 1911, global presence.  SIEM, X-Force Cloud Security. Size and resources, AI and ML focus. Organizations of all sizes have an emphasis on analytics and automation.
Okta Founded in 2009, IAM leader. IAM, SSO, User-friendly interfaces. Cloud-native approach, API-driven architecture.  Businesses of all sizes.
SOPHOS Veteran since 1983, wide range. Synchronized Security, Endpoint focus. Focus on synchronized security SMBs and enterprises 
SentinelOne Since 2013, innovative endpoint. Singularity XDR, Behavioral analysis.  Machine learning, Behavioral analysis.  Enterprises and organizations with high-value assets.
Crowdstrike Founded in 2011, Global reach and proven track record. Endpoint security, incident response, and threat intelligence. Cloud-native architecture, global threat intelligence network. Large enterprises with complex IT environments.
Tenable Established in 2004, vulnerability expert. Vulnerability management, compliance. Vulnerability prioritization, SOAR integration. Organizations of all sizes with complex IT infrastructures.
Rapid7 Founded in 2000, SIEM and VM specialist. SIEM, incident response, VM. Cloud-based SIEM, automation. Organizations of all sizes, have an emphasis on analytics and automation.

Key Takeaways

The cybersecurity industry is rapidly changing, demanding proactive and innovative solutions. Choosing the right partner is crucial for securing your organization in 2024. Strobes stands out with its focus on threat detection, unique methodologies, and proven success in protecting businesses of all sizes.

The post Top 13 Cybersecurity Companies in the USA in 2024 appeared first on Strobes Security.

*** This is a Security Bloggers Network syndicated blog from Strobes Security authored by Shubham Jha. Read the original post at: https://strobes.co/top-13-cybersecurity-companies-in-the-usa-in-2024/

Wed, 27 Dec 2023 21:44:00 -0600 by Shubham Jha on December 28, 2023 en-US text/html https://securityboulevard.com/2023/12/top-13-cybersecurity-companies-in-the-usa-in-2024/ Timely Apple updates must be in your provider SLAs
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Android Intelligence

Google Assistant's forgotten memory magic

Your Android device has a powerful system for helping you recall almost anything imaginable, but — oh, yes — it's up to you to remember to use it.


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Wall Street is gearing up for an AI shopping spree. Meet 11 bankers poised to come out on top.

Alan Bressers and Brandon Hightower, the founders of Axom Partners

Alan Bressers, left, and Brandon Hightower, the founders of Axom Partners.
Axom Partners

Bresser's relevant deal experience: Spacemaker's $240 million sale to Autodesk, NXP's proposed $47 billion sale to Qualcomm, and Linear Technology's $15 billion sale to Analog Devices.

Hightower's relevant deal experience: Magento's $1.64 billion sale to Adobe; Afterpay's $27.96 billion sale to Square, now known as Block; and Intuit's $7.1 billion acquisition of Credit Karma.

Axom Partners is one of the latest tech-centered M&A advisory firms on the Street and appears to be the first to make AI-related dealmaking the fulcrum of its business. Three alumni from the tech-advisory firm Qatalyst Partners — Bressers, Hightower, and the attorney Ross Weiner — started the firm in September and are betting on AI as a total game changer. They even used the chatbot Bard to help them come up with the name "Axom."

Bressers and Hightower told BI that Axom will focus on earlier-stage companies that rivals may view as too small, not just companies in the artificial-intelligence sector.

"We saw a chance in starting Axom Partners to service clients at transaction sizes that Qatalyst has outgrown," Hightower told BI. "We're nimble and we're building our brand to service the most innovative companies as they scale."

"We don't want to just be software bankers and AI as a part of software. We want to be able to say we understand AI down to the chip level, up to the application level, and even the impact on consumer users," Bressers said.

Bressers grew up in Pittsburgh, where his summer job in high school was giving tours of the USS Requin, a World War II-era submarine parked in the Ohio River. At 6'2", he would've been disqualified from serving on the vessel and said he had enough bumps on the head to understand why.

He graduated from Wharton and the University of Pennsylvania, where he studied economics and engineering. He started his career at Credit Suisse's San Francisco office in 2006 before joining Qatalyst in 2009. He focused on semiconductor companies there, which he says conveniently dovetailed into covering AI. He left Qatalyst in 2021. He lives in San Francisco with his wife and their 3-year-old son.

He said he's seeing companies quietly readying themselves to come out on top, including by teaming up with the companies they see as winners down the road, such as Microsoft and ChatGPT.

"They're making alliances today in order to build their companies," he said. "Those alliances will have some long-lasting impacts on them from an M&A perspective."

Hightower studied business and finance at Brigham Young University. He worked at the boutique advisory firm GCA Savvian Corporation before joining Qatalyst in 2014, where he covered consumer internet, fintech, and software companies before leaving to start Axom earlier this year.

His Christmas wish list for the next wave of AI includes an AI-supported way for marketers to reach customers better.

"If I were an Adobe or any of the marketing cloud-related companies, what I'd want under the tree is an AI solution that takes all of my customer data and helps me to really target my customers in a more automated and more cost-effective way so that I'm not spamming the wrong people and spending aimlessly on user acquisition," he said.

Hightower and his wife, Laura, welcomed their fifth child this past summer. When the big moment arrived, the couple tried to head out the door for the hospital, but the baby had other plans.

"My wife said the baby was coming, and I said yes, let's get to the hospital. I didn't realize she meant right now! You don't have time to think in those situations, so I just played catcher, and we delivered our little baby girl right there in our hallway, with my wife standing upright, only 15 minutes from the first contraction."

Hightower used zip ties and kitchen shears to cut the umbilical cord. Luckily, his wife is a nurse, so they never went to the hospital after checking in with the doctor by phone.

Bank of America's Neil Kell

Neil Kell, the chair and global head of TMT equity-capital markets at Bank of America.
Bank of America

Relevant deal experience: $500 million sale of Dynatrace stock for Thoma Bravo, Arm's IPO, Mobileye's IPO, and Intel's $1.62 million sale of its stake in Mobileye.

As the chair and global head of TMT — or technology, media, and telecom — equity-capital markets for Bank of America, Kell decides which clients need more capital to develop AI technologies and which companies would benefit from buying new IP or products.

He anticipates the AI craze to kick off a wave of fundraising activity for banks as companies across various industries — such as healthcare, aerospace, defense, and manufacturing — seek to build or acquire AI solutions.

"I do think there's going to be some very tangible M&A that's going to evolve because of this — pretty sizable and significant pick-up in capital formation. And it's all within this concept of, 'We've spent 20 years in various forms of artificial intelligence and technology. How do we monetize it now?'" Kell told BI. "That's where the Street is looking. It's all beginning to converge, so it's a pretty dynamic period."

Kell, who has been at Bank of America for 25 years, has helped clients around the world raise more than $150 billion via public and private equity financing. When not in his Palo Alto office raising money for clients, Kell likes to play the bagpipes, an instrument he's played since childhood, after his doctor suggested picking up a wind instrument to help recover from a lung injury. Kell has traveled all over the world playing the bagpipes, but one of his favorite places to play is Scotland, where he played in different competitions as a teenager.

Kell says Bank of America has dedicated bankers positioned "where there is a nexus of AI development," such as Silicon Valley and budding markets such as Austin and Denver. But he expects the bank's AI coverage to take many shapes over the coming years as its teams adapt to the constantly evolving landscape. And he expects the bank to keep investing in AI banking.

"This is something that's here to stay and something that's likely to grow and become a large and tangible part of our business," Kell said.

Citi's Sirisha Kadamalakalva

Sirisha Kadamalakalva, the head of artificial-intelligence investment banking at Citi.
Citi

Relevant deal experience: Clients include Alteryx, Cloudera, Confluent, Coveo, C3.ai, Elastic, Introhive, Klaviyo, and MuleSoft.

Kadamalakalva joined Citi in January to head the bank's artificial-intelligence investment banking efforts — and it's been a whirlwind ever since.

The sector is advancing so fast it's "dizzying," she said in an interview. She equated one year in AI to five to 10 years in other sectors.

"We are so early in the cycle that evaluating winners and losers is almost an everyday exercise," she said.

Because AI impacts other sectors and subsectors within technology, Kadamalakalva often works with bankers without tech experience to bring her expertise to their respective industries.

Before joining Citi, she worked as a software-investment-banking unit leader at Bank of America for nearly a dozen years. But she says working with AI companies is far different from working with traditional software.

"There's a lot of strategic challenges that these companies deal with," she said. "It's not just about being a banker, but a strategic partner."

To that end, Kadamalakalva tries to build relationships with budding AI companies — some of which will hopefully become her clients and mint her millions of dollars — early on.

Looking to 2024, Kadamalakalva said tangential technologies that directly impact AI's growth — or, put another way, the ingredients that add up to make AI possible — will shape AI dealmaking. That's why Kadamalakalva is monitoring tech trends such as the shortage of GPUs, which are special and expensive chips critical to training AI models.

Goldman Sachs' Jung Min

Jung Min, a partner at Goldman Sachs.
Goldman Sachs

Goldman Sachs' Jung Min

Relevant deal experience: Microsoft's $69 billion acquisition of Activision Blizzard; McAfee's $14 billion sale to investors who took it private; and Intuit's $8.1 billion acquisition of Credit Karma.

To best navigate AI M&A in 2024, bankers will need to be more like they were in 1990, Min, the co-COO of Goldman Sachs' technology, media, and telecom division, said.

"In the 90s, people would've said, 'I'm a tech banker.' They would not have said, 'I'm a software banker,' or 'I'm a semiconductor banker.' But we kind of need to go back to doing that," Min told BI.

That's because AI touches so many different layers of the tech stack, or the infrastructure that makes it possible to develop and run AI applications. That means bankers specializing in software companies are melding more with semiconductor bankers. And other bankers, including Min, are working to cut across the different layers and advise on all aspects of tech.

For his part, Min covers the hyperscalers, or the large cloud companies that have made names for themselves developing front-end software services. But the same cloud companies have also started developing their own GPU chips, which are in short supply but are essential for training the big models behind the latest AI tools. Min also covers large semiconductor companies building GPUs "because I need to know what's going on across the different stacks or different layers of the tech stack," he said.

That approach appears to work for Goldman Sachs, which played a critical role in Microsoft's acquisition of Nuance Communications in April 2021 for $19.7 billion, S&P Global Market Intelligence data shows.

It's still early in the AI M&A cycle, but Min sees AI deal activity accelerating as more companies realize their AI shortcomings. That could lead to tech companies looking to acquire one specific layer of the tech stack — such as data analytics — needing to acquire other parts, such as data storage or computing.

Min also expects to see non-tech companies with strong customer bases and lots of data get into the AI game. "They're going out to buy the tech companies that do have those AI capabilities, so that they can create those products and they can monetize the value," Min said.

JPMorgan's Madhu Namburi

Madhu Namburi, the global head of technology investment banking at JPMorgan.
JPMorgan

Relevant deal experience: $69 billion sale of VMware to Broadcom; Qualtrics' $12.5 billion sale to Silver Lake, who took it private; $18.5 billion sale of Worldpay to GTCR.

A JPMorgan spokesperson said that Namburi sets the overall strategy for JPMorgan's technology practice. That includes capital-allocation decisions and client prioritization for corporate clients across software, fintech, and other tech sectors.

According to his LinkedIn, Namburi, who declined to be interviewed for this list, has a bachelor's degree in mechanical engineering from Delhi College of Engineering. He joined JPMorgan in 2000 after earning his MBA from Pennsylvania State University.

Last year, Namburi spoke optimistically about the growth of the tech sector in a video JPMorgan posted to its Facebook page. He said that right now, five of the largest companies in the world are tech companies. In 10 years, he expects that number to get bigger, not smaller.

"This rate of value creation is going to only continue," he said in the video.

When it comes to AI and data, the tech world is "only just scratching the surface" in terms of capability and growth, he said in a separate video posted to X.

The bank said he has experience in M&A, growth IPOs, LBOs, debt and equity, and equity-linked financing across large and smaller growth-oriented technology companies.

He also manages a tech venture-investment fund that has invested in various emerging companies within the technology sector focused on AI, data analytics, enterprise infrastructure, and disruptive business models, JPMorgan said in an email.

After spending 15 years in JPMorgan's New York office, he moved to the San Francisco Bay Area about eight years ago. The firm said he lives in Hillsborough with his wife Radhika, their 18-year-old daughter Ila, and their 15-year-old son Niam.

Lazard's John Gnuse

John Gnuse, the managing director at Lazard.
Lazard

Relevant deal experience: Google's $2.6 billion acquisition of the data-analytics business Looker in 2020; Intel's $2 billion acquisition of the AI-chip company Habana Labs in 2019.

Gnuse is a Lazard lifer — he joined the firm in 1992 as an investment-banking analyst and has been with the company ever since. The only time he spent away was in the mid-90s for his two-year grad-school program at the University of Cambridge, where he studied history and philosophy.

Gnuse has covered the tech sector at Lazard through decades of changes, starting in New York and then in San Francisco, where he moved in 1999 during the dot-com bubble. In exact years, his team has represented large-cap technology companies, including Google, IBM, and Intel, at the center of the AI conversation.

He said he thinks the winners of the AI frenzy will be the companies that integrate AI capabilities into existing tools and workflows. "Application providers that can embrace these capabilities quickly and provide incremental value to their end users have the potential to capture a big part of the value," he said. "And ones that don't could face growing questions from investors about the threats of AI to their core business."

Gnuse said he's seeing a lot of activity in application areas where generative-AI capabilities have already proven relevant, including code development and customer support, and in fields such as law and education, where paperwork is necessary but tedious.

"These are areas where outputs often follow a very standard form or syntax, and you can train models to mimic that pattern," he said — companies with a relevant user base in these domains could be where we see early activity.

Gnuse is no stranger to academia — he majored in physics and philosophy at Yale as an undergrad, has a master's degree in history from the University of Cambridge, and an MBA from INSEAD Business School — and says it's important to be in a sector you love to study. His career advice to the next generation of dealmakers is to align themselves to a sector or subsector they're passionate about like he did with technology.

"Thirty years ago, many top bankers could afford to be generalists and somewhat sector agnostic, but today, clients really value advisors who intimately understand the nuances of their business and sector-specific strategic issues," he said. "Find a segment that you love to research and study. I'm blessed that I get to work in tech because there are fascinating developments every day."

Morgan Stanley's David Chen

Dave Chen, the head of global technology investment banking at Morgan Stanley.
Morgan Stanley

Relevant deal experience: IPOs for Barracuda Networks, C3.ai, and Salesforce; $28 billion sale of Splunk to Cisco; sale of Archer Technologies to Cinven; Thales' $3.6 billion acquisition of Imperva from Thoma Bravo.

Chen started his career at UBS Wealth Management in 1998 but has been at Morgan Stanley since 2002. He traces his interest in the sector back to his college days.

"From the time that I was an undergrad at Stanford, I have been hooked on technology," Chen told BI via email.

That early interest has held strong through his 24-year career. Now at the helm of Morgan Stanley's technology-investment-banking practice in Menlo Park, California, Chen has worked on some of the most formative deals in the AI space yet — including as an advisor to Splunk in its historic $26 billion sale to Cisco.

"I have had a front-row seat to the transformation of the technology industry while advising industry stalwarts over the years. The dynamism and innovation of tech have kept me deeply interested over the years, and I anticipate this to continue to be the case for many more to come."

If there's a road map for future deals we can glean from the historic Splunk acquisition, it's that 2024 deals will value two things: data and talent.

"AI makes the data a company has even more powerful," he said. "So far, most are looking to take data and talent and then create an in-house solution or partner with other providers instead of acquiring AI-based products directly."

The way he sees it, M&A in the AI space has been muted compared to the surge in interest for several reasons. First, valuations are high. Second, large companies under pressure to move quickly are considering other solutions, including building their tech in-house or partnering with a large language model provider. He expects that trend to continue before unleashing a torrent of activity.

"I'm expecting a slow burn in the first half of the year with companies still determining their AI product strategy and then a boom of activity as companies gain conviction in their plans and as macro pressures are reduced," he said. "Acquisitions will mostly be about accelerating product roadmaps, and I think 2025 would be the year that we see some very exciting IPOs in this sector."

AI will not only influence deals, he said, but also impact the way tech-banker bosses, including Chen, run their teams.

"AI copilots and AI research bots will infuse everything we do over time," he said. "From creating presentation materials to helping our bankers conduct market research, this will be more transformative to our work than search engines have been."

Qatalyst's Rob Chisholm

Rob Chisholm, a partner at Qatalyst Parnters.
Qatalyst Parnters

Relevant deal experience: Tableau's $15.7 billion sale to Salesforce; Qualtrics' $12.5 billion sale to Silver Lake and CPP Investments; Zendesk's $10.2 billion sale to Hellman & Friedman and Permira.

Chisholm thinks of AI as a total game changer for the industry, the likes of which we haven't seen since Apple rolled out its groundbreaking smartphone.

"People talk about the 'iPhone moment' for gen AI. There really is this sort of pivot moment in history that we're all going to look back on and see as the moment when our world changed in, frankly, very unpredictable ways, but mostly in exciting ways."

Chisholm, who joined Qatalyst in September, helps lead the enterprise-software group and spearheads the firm's AI efforts. He says the speed and disruptiveness of AI have made liaising between companies a bigger part of his job.

"Almost overnight, all of these people — both the disruptive people starting new companies and the established, successful large software and broader technology companies — they all wanted to be talking to each other," he said.

He was previously a partner at Goldman Sachs, where he spent five years on its TMT investment-banking team.

He's the first to admit that his Wall Street story is unusual. Hailing from a small town in Nova Scotia, he went to Princeton on a hockey scholarship. Still, he left in favor of a small liberal-arts school in Vermont, Middlebury College, to study environmental policy. At the time, everyone told him he was making a mistake.

After graduating, Chisholm worked at an environmental nonprofit in Boston, but after finding the work "a little bit lower intensity than suited my personality," he took a job at the investment-banking boutique AGC Partners, later working at Deutsche Bank and then Citi before ultimately ending up at Goldman Sachs in 2018.

He clearly remembers when he knew Qatalyst would be the right move during a conversation with the firm's famous founder.

"When I interviewed with Frank Quattrone, I asked him, 'What will dictate whether I'm successful at Qatalyst or not?' And he said, 'Come to Qatalyst if you want to be an active participant in how the technology industry changes and not just a passive agent of what is happening around you.' I almost jumped out of my chair when he said that to me."

"Of all the things that investment bankers do, M&A is by far the most interesting to me," he said. "And that is everything we do at Qatalyst. You can wake up on a Monday morning, and the world will be different than it was Sunday night when that deal you were working on for six months or a year gets announced."

Tidal Partners' David Handler and David Neequaye

David Handler, left, and David Neequaye, the cofounders of Tidal Partners.
Tidal Partners

Handler's relevant deal experience: Cisco Systems' $28 billion acquisition of Splunk, ServiceNow's acquisition of G2K, and Bloom Energy on convertible notes offerings.

Neequaye's relevant deal experience: Mixpanel's $200 million in Series C funding from Bain Capital; Motorola Inc.'s $9 billion spinoff of its Mobility & Connected Home businesses; Motorola Mobility on its $12 billion sale to Google.

Starting a new business is risky, especially in the stodgy world of investment banking. But Handler and Neequaye knew they could leverage their years of dealmaking experience.

"The relationships and trust we've built over the last two decades and our proven track record of industry-defining transactions serve to differentiate the value Tidal will bring to our clients," Handler said in an August 2022 release announcing the launch of Tidal Partners.

Despite being one of the latest M&A firms in the space, Tidal Partners is already making a name for itself in the AI landscape, advising on Cisco System's $28 billion acquisition of Splunk and ServiceNow's acquisition of the AI platform G2K.

"We've known David (Handler) and his partner David (Neequaye) for a very long time," Chuck Robbins, the CEO of Cisco, told Reuters in September after announcing the pending acquisition of Splunk. "They did a great job for us."

Handler and Neequaye, who declined to be interviewed for this story, have a long history together. Before Tidal, they were founding members of the technology practice at Centerview Partners, where they worked for 14 years, according to their LinkedIn profiles.

Following his 2022 departure, Handler sued Centerview over a pay dispute. Handler and Neequaye also overlapped at UBS, where Handler was the cohead of technology investment banking, and Neequaye was the director of technology investment banking, according to their LinkedIn profiles. Both men also worked at Bear Stearns in the early 2000s.

In launching Tidal, Handler said: "The tech landscape is more dynamic and evolving faster than ever. We see an opportunity for a trusted strategic partner who will help clients connect dots and move with greater agility and creativity."

Mon, 25 Dec 2023 20:00:00 -0600 en-US text/html https://www.businessinsider.com/top-investment-bankers-for-generative-ai-acquisitions-capital-raising-advice




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