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Killexams : CyberArk CyberArk techniques - BingNews Search results Killexams : CyberArk CyberArk techniques - BingNews Killexams : CyberArk, Delinea, One Identity Top Gartner MQ for PAM CyberArk Keeps Leading the PAM Market, With Delinea and One Identity Close Behind
CyberArk, Delinea, One Identity Top Gartner MQ for PAM

CyberArk better be careful - that's the gist of a new study of the privileged access management market. Long-reigning undisputed leader it may be, but it's not impervious to competitors such as Delinea and One Identity, which are catching up thanks to a few acquired boosts last year to their capabilities.

See Also: OnDemand | Zero Tolerance: Controlling The Landscape Where You'll Meet Your Adversaries

"CyberArk has been the 800-pound gorilla for a long time. They've been the king of the hill," Gartner Magic Quadrant author Michael Kelley tells Information Security Media Group. "They still remain the leader, but I think lots of companies are doing a lot of investment and catching up a little bit on feature and functionality." The consultancy released its latest Magic Quadrant for the PAM industry on July 19, evaluating the competitive standings of 11 companies.

Gartner once again recognized publicly traded Boston-area vendor CyberArk for having the most complete vision and strongest execution ability around privileged access management.

Reasons for the company to closely watch its back paradoxically start with the growing size of the market. Interest in PAM tools has perked up outside its traditional constituency of compliance and audit departments, Gartner finds. Cyber insurance providers are almost bullying corporate Americans into becoming PAM customers by threatening massive rate increases to any client without such tools, Kelley says.

"The cost of a breach is much larger if a privileged account has been compromised," Kelley says. "I think cybersecurity insurers recognize that one way of limiting the impact of cybersecurity breaches - and especially their responsibility to pay for cybersecurity breaches - is to have good controls in place for privileged accounts." Gartner estimated in 2018 that 70% of organizations would implement PAM by 2022, and that prediction has nearly come to pass.

CyberArk competition One Identity, which is splitting from Quest and is fresh from its October purchase of OneLogin, took silver for completeness of vision in the Gartner race. Mumbai-based Arcon took the silver in execution ability.

Silicon Valley-based Delinea was formed in February through the merging of TPG Capital-owned rivals Thycotic and Centrify and was bestowed bronze in both completeness of vision and execution ability. Atlanta-based BeyondTrust and Paris-based Wallix were also recognized as leaders by Gartner.

"Some people are surprised because Arcon and Wallix are more regional companies," Kelley says. "But they've become very, very strong in their region. And they've become very strong from a technical perspective. So we felt that they belonged."

Gartner's take on the privileged access market has changed somewhat from last year, with Wallix progressing from a challenger to a leader thanks to increased innovation, more interesting things on the road map, and a better vision for approaching the market. And BeyondTrust - whose execution ability last year was head and shoulders above all leaders but CyberArk - now ranks fifth in the execution criteria.

Outside of the leaders, here's how Gartner sees the privileged access management market:

  • Visionary: Saviynt;
  • Niche Players: Broadcom, ManageEngine, Hitachi ID and Netwrix;
  • Missing the List: Fudo Security, HashiCorp, Imprivata, Krontech, Microsoft, Remediant, Sectona, Senhasegura and Teleport, which didn't meet technical or revenue inclusion criteria.

"PAM is a fundamental, foundational security control that you just can't do without nowadays," Kelley says.

CyberArk Seeks to Boost On-Premises Experience

CyberArk has focused over the past year on extending its endpoint privilege manager product to include Linux in addition to Windows and Mac so that firms can more easily elevate users to admin privileges no matter which operating system they're using, says Barak Feldman, senior vice president of PAM and identity security. The company wants to more effectively address use cases in the cloud, SaaS and DevOps world, he says (see: CyberArk Execs: 9 Bets on What's Next in Identity Security).

With CyberArk's on-premises PAM product, Feldman says the investments have focused primarily on user experience to Boost time to value and the experience around deployment. CyberArk's maturity in the PAM market has allowed it to expand into adjacent areas such as just-in-time access and cloud entitlements while competitors are still focused on maturing and adding core features to their PAM offering, he says.

"Our foundation is so strong that we have the ability now to expand into areas like just-in-time access and cloud entitlements," Feldman tells ISMG. "Our competitors are still in the phase of getting that experience maturing. And so we're very excited about the results."

Gartner criticized CyberArk for poor ease of use and difficulty deploying the software-delivered version of the product. Feldman says CyberArk wants to make its on-premises product easier to manage and upgrade and ensure that the rich features it offers don't introduce additional complexity. Roughly two-thirds of CyberArk PAM subscriptions today are SaaS-based with the remaining one-third still on-premises.

"We definitely are putting a lot of effort to make the customer experience a lot better," Feldman says. "It's a continuous thing."

Delinea Puts Usability Front and Center

Bringing Thycotic and Centrify together has created a best of both worlds scenario thanks to Thycotic's strength in vaulting, managing, collecting and rotating credentials and Centrify's ability to define what users entering the system are allowed to access, says Delinea CEO Art Gilliland. Delinea will incorporate elevated risk scoring alongside privilege accounts when deciding which users get what level of access.

Gilliland says Delinea continues to focus on enhancing the usability of its workflows and interface given the skills shortage and level of attrition many of the company's customers are experiencing. In addition, Delinea wants to consolidate its account management capabilities and apply the principles of least privilege to both traditional super users as well as devices and nonuser-based identities, Gilliland says.

"Products that are intuitive and easy to use are just more effective because you'll use them," Gilliland tells ISMG. "We've invested a lot in upgrading the UI and making the interface a lot more usable."

Gartner criticized Delinea for lacking native agentless recording in its session management offering and lagging behind in service account and credential management for Secret Server. Gilliland says the lack of certain features reflects the strategic choice Delinea has made to focus largely on core use cases without creating custom tooling that would make the product heavier and more complicated for everyone else.

"This allows us to focus very specifically on the core use cases without dragging us down the rabbit hole of individual company integrations, which some of our competitors spend a lot of resource on," Gilliland says. "Part of our differentiation is that the technology is fast and easy to use."

One Identity Sees PAM as Part of Bigger Picture

Although PAM accounts for more than half of One Identity's deployments, customers benefit from the company's presence in the identity governance, access management and Active Directory management and security markets following last year's acquisition of OneLogin, says Larry Chinski, vice president of global IAM strategy and customer advocacy. This allows One Identity to have more than just a stand-alone PAM tool.

One Identity has differentiated its privileged access practice by putting the remediation of events in real time using behavioral biometrics front and center rather than the elevation of privileged accounts, Chinski says. Moving away from the fragmented, siloed PAM tools that exist in infrastructure-focused security postures and toward a more holistic platform featuring PAM will benefit customers, he says.

"Cybersecurity has changed in just the last couple of years," Chinski tells ISMG. "It's moved from an infrastructure-centric protection model to an identity-centric 'verify and validate' model. You're not building your security posture around infrastructure components anymore. Instead, we're building a security posture on top of the identity."

Gartner criticized One Identity for offering only rudimentary stand-alone secret management capabilities and lacking the critical SOC 2 certification for its SaaS offering. One Identity already had plans to address the product and operational deficiencies highlighted by Gartner while preparing for use case feature consolidation by tying its platform back to identity management for privileged governance.

"The-next generation PAM is about being able to integrate with IAM for web single sign-on to the PAM portals and all the benefits we get by snapping into IGA for governance and Active Directory management for zero trust," Chinski says.

BeyondTrust to Join On-Prem, Cloud on Single Console

BeyondTrust has been taking the functions used to manage privileged accounts on-premises and moving them into Amazon Web Services or Microsoft Azure to give customers a more unified understanding of their cloud environment, says CTO Marc Maiffret. The company will debut a platform late this year that will allow customers to manage on-premises, cloud and SaaS users from a single console, Maiffret says.

Maiffret wants BeyondTrust to get to the heart of the identity threat detection space through policies, compliance and controls that can lessen the impact of a breach and better understand the identities in an environment that are being compromised. BeyondTrust plans to work with third-party security operations and identity teams to Boost the detection of identities that have been compromised.

"The biggest push across the entire suite of products has really been around how we extend them to cloud service provider and SaaS environments," Maiffret tells ISMG. "It's about taking the timeless aspects of security, like least privilege, and moving that into cloud environments."

Gartner criticized BeyondTrust for low privileged session management scores and lacking support for native authentication mechanisms in its secrets management offering. Maiffret says the critique stems from Gartner not including BeyondTrust's privileged remote access product as part of the evaluation, adding that BeyondTrust's unified platform coming in late 2022 wasn't reflected in Gartner's evaluation.

"We're not too thinking because we're more interested in what actually comes out at the end of the year and what we're working on," Maiffret says.

Wallix Expands From Europe to the US

Wallix entered the PAM market three years ago from a session management background to provide customers with a unified way to control access and privilege for any kind of user, says product and marketing director Edwige Brossard. The company recently made it easier to delegate and elevate privileged access so that companies can provision heightened access for a defined period of time.

Brossard says Wallix has implemented a remote SaaS service as part of its portfolio to make it easier for organizations to deploy and maintain their privileged access offering. Wallix has also invested heavily in certain vertical markets such as OT to deliver manufacturers and members of the supply chain cyber expertise and better usability without compromising on protection, according to Brossard.

"We do believe that having something more unified and dealing after that with privileges as a task you have to cover is really important," Brossard tells ISMG.

Gartner criticized Wallix for lacking native features for privileged access governance and administration and offering only limited account discovery features. Brossard says Wallix needs to expand the breadth of its product offering and already had a lot of the shortcomings Gartner noted on its product road map. Wallix's push into the United States this year will make the company more visible to global customers.

"Our plan is to keep pushing what we have in the U.S. and accelerate the customer base over there while at the same time developing much more of an MSP and midmarket business in Europe, Brossard says.

Arcon Continues to Play Feature Catch-Up

The Gartner Magic Quadrant report lauds Arcon for making big strides around secrets management, CIEM, and just-in-time functionality over the past year and pricing its privileged account and session management capabilities below most of its peers. Gartner praises Arcon for giving each customer a customer success manager and making its technical account managers available for large accounts.

Gartner criticized Arcon for having a limited number of prebuilt integrations for adjacent technologies and relying on agents and client tools for the best user experience. The company lags behind its peers in gaining new customers, with Arcon's road map focusing largely on adding new features that already exist in competitor products. Arcon didn't respond to an ISMG request for comment.

"A big thank you to our global partners and customers as well," Arcon writes in a blog post. "Your unwavering support keeps Arcon motivated as it continues to challenge every boundary."

Fri, 29 Jul 2022 10:46:00 -0500 en text/html
Killexams : Gaining privileged access still the main target for cyber attackers

Despite the numerous identity security tools being implemented, attackers are still finding ways to gain privileged access to organizations. Social engineering attacks for example specifically target individuals whom cybercriminals know can enable them to get privileged access.

In fact, Budiman Tsjin, Solutions Engineering Manager for ASEAN at CyberArk pointed out that cyber attackers are establishing initial entry points into target organizations. Credential theft methods like spear-phishing and impersonation are as popular as ever.

In the second part of Tech Wire Asia’s conversation with Tsjin, he explains the common techniques attackers use to gain privilege access as well as the challenges in verifying digital identities.

What are the common techniques attackers are using to try to gain privileged access?

Budiman Tsjin, Solutions Engineering Manager, ASEAN at CyberArk

The global shift to remote work and e-learning, and large-scale investments in Software-as-a-Service (SaaS) and cloud services have brought about a surge in newfound technologies and identities. Companies have ramped up their investments in digital transformation.

The 2022 Verizon Data Breach Investigations Report (DBIR) found that 82% of breaches involved the human element, among which close to 40% of attacks are attributed to stolen credentials. Stolen credentials soared as the most common attack vector in 2021.

Since employees are increasingly conducting their personal lives online, it’s becoming easier for attackers to gather the necessary information required to execute their social engineering campaigns.

Cyber attackers have changed the individual targets of these social engineering attacks. Traditionally, adversaries focused their attention on IT admins with highly privileged access. But they’re now going after new user populations, from executives and software developers to end-user employees, including business users with direct access to sensitive data or systems the attacker is interested in.

Employees or contractors with high-value access are becoming more interesting targets for attackers for several reasons. Emphasis has also shifted to end-users on the edge because it’s becoming more difficult to compromise IT admin accounts. Many organizations are aware that damaging breaches occur when attackers obtain powerful admin credentials and have put strong controls in place through a privileged access management system. As more organizations move to a Zero Trust model, more endpoints connect to resources directly rather than being given broad access.

What are the challenges faced when it comes to verifying digital identities?

Most organizations have not prioritized the protection of critical data and assets. Instead, they’re moving full steam ahead with initiatives, such as full integration of complex IT infrastructures to the cloud-based systems. This could pose a significant risk to the protection of their data.

As the lines continue to blur between identity and privilege, organizations need ways to confidently verify that workforce identities can confirm that they are indeed who they say they are, that their devices are Checked and that their access is intelligently limited to exactly what’s required.

This verification is done through adaptive multi-factor authentication and tools like single sign-on, coupled with behavior-based machine learning that can make intelligent access decisions in real-time based on user context and risk. When organizations adopt Zero Trust, this ensures every user’s identity is verified, their devices are validated, and their access is limited to just what they need – and taken away when they don’t.

Why are some businesses still struggling with adopting zero trust and other identity-based security approaches?

Some of the challenges arise due to an organization’s lack of resources and budget allocation for security solutions for their IT systems. In addition, finding the right aspects of cybersecurity to prioritize can present significant challenges. Organizations often lack sufficient resources to deal with emerging threats from both a personnel and budget standpoint.

Thus, it’s important to think about how security can be a business enabler, and not a blocker, for an organization. If you’re a security leader, you want to help drive the conversation with leaders from the business side about the value of applying strong cybersecurity to modern technologies, not only to mitigate risk and maintain a strong security posture but also to Boost operational efficiencies.

Another area of concern is the lack of C-level buy-in for the adoption of Zero Trust. While we have seen an improvement in this area, over the past year or so, especially in the Asia Pacific region, there is still room for improvement.

By setting the right tone from the top, an organization can help to ensure the successful deployment of Identity Security controls across the enterprise. Although security will drive the project, the affected systems are owned by the business and will require cross-functional support.

Some stakeholders will balk at the changes that have to be made, such as giving up access rights or following new processes that may cause additional inconveniences. Aside from having a clear direction from leadership, change management is also crucial to get buy-in from employees and increasing the adoption of security tools within the organization, thus improving the organization’s overall security posture.

Lastly, how is CyberArk helping businesses Boost their digital trust?

Today, for every human identity, there are 45 machine identities, and over half of an organization’s workforce has access to sensitive corporate data. These human and machine identities represent an expanded attack surface that adds pressure to mounting cybersecurity insurance and compliance requirements.

These challenges call for advanced Identity Security solutions architected for the evolving threat landscape with the ability to enforce least privilege. Identity Security seamlessly secures access for all identities, and flexibly automates the identity lifecycle, with continuous threat detection and protection – all with a unified approach.

Wed, 27 Jul 2022 11:30:00 -0500 en-US text/html
Killexams : CyberArk Software: This Security Stock Remains An Odds-On Buy
Business on Wall Street in Manhattan

Pgiam/iStock via Getty Images

Investment Thesis

The trade-commission-free automation progress achieved by markets in serving a continuing flow of individual investor internet-order small trades makes it necessary for Market-Makers ("MM") to have capital at risk while handling the irregular huge-value “institutional” transactions. They protect their at-risk capital endangerment by hedging actions which reflects the coming price range expectations of the stocks involved – virtually all of the actively-traded issues.

The pricing and structure of such hedges reveal the coming-price expectations of both the MM protection-buyers and that of the MM industry protection-sellers.

Our selection of CyberArk Software Ltd. (NASDAQ:CYBR) is prompted by its currently-attractive stock pricing coupled by a large following of Seeking Alpha readers.

Description of Subject Company

“CyberArk Software Ltd., together with its subsidiaries, develops, markets, and sales software-based security solutions and services in the United States, Europe, the Middle East, Africa, and internationally. Its solutions include Privileged Access Manager that offers risk-based credential security and session management to protect against attacks involving privileged access to provide fast, easy, and secure privileged access to third-party vendors. The company provides its products to financial services, manufacturing, insurance, healthcare, energy and utilities, transportation, retail, technology, and telecommunications industries; and government agencies through direct sales force, as well as distributors, systems integrators, value-added resellers, and managed security service providers. CyberArk Software Ltd. was founded in 1999 and is headquartered in Petah Tikva, Israel.”

Source: Yahoo Finance.

Street analyst estimates

Yahoo Finance

Risk~Reward Comparisons of Portfolio Investment Candidates

Figure 1

MM hedging forecasts

(used with prior permission)

The tradeoffs here are between near-term upside price gains (green horizontal scale) seen worth protecting against by Market-Makers with short positions in each of the stocks, and the prior actual price drawdowns experienced during holdings of those stocks (red vertical scale). Both scales are of percent change from zero to 25%.

The intersection of those coordinates by the numbered positions is identified by the stock symbols in the blue field to the right.

The dotted diagonal line marks the points of equal upside price change forecasts derived from Market-Maker [MM] hedging actions and the actual worst-case price drawdowns from positions that could have been taken following prior MM forecasts like today's.

Our principal interest is in CYBR at location [1]. A "market index" norm of reward~risk tradeoffs is offered by SPDR S&P500 index ETF (SPY) at [4].

Those forecasts are implied by the self-protective behaviors of MMs who must usually put firm capital at temporary risk to balance buyer and seller interests in helping big-money portfolio managers make volume adjustments to multi-billion-dollar portfolios. The protective actions taken with real-money bets define daily the extent of likely expected price changes for thousands of stocks and exchange-traded funds ("ETFs").

This map is a good starting point, but it can only cover some of the investment characteristics that often should influence an investor's choice of where to put his/her capital to work. The table in Figure 2 covers the above considerations and several others.

Comparing Alternative Investments

Figure 2

detail comparative data

(used with permission)

Column headers for Figure 2 define elements for each row stock whose symbol appears at the left in column [A]. The elements are derived or calculated separately for each stock, based on the specifics of its situation and current-day MM price-range forecasts. Data in red numerals are negative, usually undesirable to “long” holding positions. Table cells with pink background “fills” signify conditions typically unacceptable to “buy” recommendations. Yellow fills are of data for the stock of principal interest and of all issues at the ranking column, [R].

Readers familiar with our analysis methods may wish to skip to the next section viewing price range forecast trends for CYBR.

Figure 2’s purpose is to attempt universally comparable measures, stock by stock, of a) How BIG the price gain payoff may be, b) how LIKELY the payoff will be a profitable experience, c) how soon it may happen, and d) what price drawdown RISK may be encountered during its holding period.

The price-range forecast limits of columns [B] and [C] get defined by MM hedging actions to protect firm capital required to be put at risk of price changes from volume trade orders placed by big-$ "institutional" clients.

[E] measures potential upside risks for MM short positions created to fill such orders, and reward potentials for the buy-side positions so created. Prior forecasts like the present provide a history of relevant price draw-down risks for buyers. An average of the most severe ones actually encountered are in [F], during holding periods in effort to reach [E] gains. Those are where buyers are most likely to accept losses.

[H] tells what proportion of the [L] demo of prior like forecasts have earned gains by either having price reach its [B] target or be above its [D] entry cost at the end of a 3-month max-patience holding period limit. [ I ] gives the net gains-losses of those [L] experiences and [N] suggests how credible [E] may be compared to [ I ].

Further Reward~Risk tradeoffs involve using the [H] odds for gains and the 100 - H loss odds as weights for N-conditioned [E] and for [F], for a combined-return score [Q]. The typical position holding period [J] on [Q] provides a figure of merit [fom] ranking measure [R] useful in portfolio position preferencing. Figure 2 is row-ranked on [R] among candidate securities, with CYBR yellow-row identified.

Along with the candidate-specific stocks these selection considerations are provided for the averages of over 3,000 stocks for which MM price-range forecasts are available today, and 20 of the best-ranked (by fom) of those forecasts, as well as the forecast for SPY as an equity market proxy.

Recent Trends in MM Price-Range Forecasts for CYBR

Figure 3

MM daily forecast trend

(Used with permission).

This picture is not a “technical chart” of past prices for CYBR. Instead, its vertical lines show the past 6 months of daily price range forecasts of market actions yet to come in the next few months. The only past information there is the heavy dot of the closing stock price on the day of each forecast.

That data splits the price ranges’ opposite forecasts into upside and downside prospects. Their trends over time provide additional insights into coming potentials, and helps keep perspective on what may be coming.

The small picture at the bottom of Figure 3 is a frequency distribution of the Range Index’s appearance daily during the past 5 years of daily forecasts. The Range Index [RI] tells how much the downside of the forecast range occupies of that percentage of the entire range each day, and its frequency suggests what may seem “normal” for that stock, in the expectations of its evaluators’ eyes.

Here the present level is near its least frequent, lowest-cost presence, encouraging the acceptance that we are looking at a realistic evaluation for CYBR. Many of past RIs have been above the present RI, indicating there is more room for an even more positive outlook.

Investment Candidates' Prior Profitability Prospect

Figure 4

TRMD risk-management discipline

(Used with permission).

This comparison map uses an orientation similar to that of Figure 1, where the more desirable locations are down and to the right. Instead of just price direction, the questions are more qualitative: “how big” and “how likely” are price change expectations now?

Our primary interest is in CYBR’s qualitative performance, particularly relative to alternative investment candidate choices. Here CYBR is at location [5], the intersection of horizontal and vertical scales of +20% gain and +88% assurance (ODDS) of a “win.”

As a market norm, SPY is at location [6] with a +3% payoff and a 72% assurance of profitability. CYBR tends to dominate all the return payoffs in this comparison.


Among these alternative investments explicitly compared, CyberArk Software Ltd. appears to be a logical buy preference now for investors seeking near-term capital gain.

Question: Is this form of comparisons more or less useful to you in your investment choice selections than one geared to industry economics or competitive actions?

Tue, 02 Aug 2022 02:56:00 -0500 en text/html
Killexams : Eaton, CyberArk Join to Deliver Automated Solution Securing Critical Utility Grid Automation Devices and Networks

Pittsburgh, PA. (Aug. 5, 2022) — Intelligent power management company Eaton announced a cybersecurity collaboration with global leader in identity security CyberArk to enhance protection of utility transmission and distribution devices and networks. The collaboration leverages Eaton’s grid automation expertise and industry-leading approach to cybersecurity with CyberArk’s Identity Security solutions to help utilities simplify a critical element of cybersecurity compliance for modern grid automation systems.

“Today’s utility infrastructure is more connected than ever before, which provides unprecedented grid flexibility and stability alongside significant cybersecurity risk,” said Eric Lebeau, product line manager for Eaton. “By working with CyberArk, we are uniquely able to help utilities unify cybersecurity monitoring and management of both OT and IT devices – regardless of manufacturer – to secure the grid and streamline industry compliance.”

The ongoing decentralization and digitalization of power makes it essential for utilities to safeguard connected devices and data against the rising threat of cyberattack. By integrating CyberArk’s capabilities into its IED Manager Suite (IMS), Eaton is enabling utilities to automate the complex task of manually verifying, managing and rotating network access passwords for intelligent electronic devices (IEDs) used in grid automation systems. The CyberArk integration within Eaton’s IMS software also allows utilities to retrieve credentials easily and securely for scans and firmware updates.

“Critical infrastructure continues to be vulnerable to cyber attackers seeking to disrupt operations or cause harm,” said Clarence Hinton, chief strategy officer and head of corporate development, CyberArk. “We believe this integration with Eaton will provide joint customers with greater control and protection of those credentials used to access sensitive systems, especially those credentials that have privileged access. We look forward to continuing to expand our collaboration and work together with Eaton to help drive down risk and exposure to cybersecurity threats.”

Eaton’s IMS software helps utilities manage configuration settings, passwords and firmware for IEDs from a wide range of manufacturers used in grid automation systems. It reduces maintenance costs through secure remote access and helps comply with North American Electric Reliability Corporation (NERC) critical infrastructure protection (CIP) requirements.

Fri, 05 Aug 2022 08:20:00 -0500 en-US text/html
Killexams : Why enterprises face challenges in protecting machine identities

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Most enterprises do not know how many machine identities they’ve created or what the levels of security are for those identities, making protecting them a challenge. It is common knowledge among CISOs that tracking workload-based machine identities is difficult and imprecise at best. As a result, up to 40% of machine identities aren’t being tracked today. Adding to the challenge is how overwhelmed IT, and cybersecurity teams are. 56% of CISOs say their teams are overextended in supporting digital transformation initiatives, struggling to get cybersecurity work done.   

Enterprises are having trouble keeping up 

Machine identities now outweigh human identities by a factor of 45 times, the typical enterprise reported having  250,000 machine identities last year. Additionally, a exact survey from Delinea found that just 44% of organizations manage and secure machine identities, leaving the majority exposed and vulnerable to attack. Another challenge that companies face is automating digital certificate management, alleviating the potential for enterprise-wide breaches comparable to SolarWinds and Nvidia’s stolen code signing certificates being used to sign malware. Table stakes for any zero-trust strategy is an automated, secure approach for managing certificates.

Keyfactor’s 2022 State of Machine Identity Management Report found that 42% of enterprises still use spreadsheets to track digital certificates manually, and 57% don’t have an accurate inventory of SSH keys. The exponential growth of machine identities combined with sporadic protection from IAM systems and manual key management is driving an economic loss estimated to be between $51.5 to $71.9 billion from compromised machine identities.

What’s needed to protect machine identities 

Identity access management (IAM) systems need tools for managing machine lifecycles designed into their architectures that support applications, customized scripts, containers, virtual machines (VMs), IoT, mobile devices, and more. In addition, machine lifecycles must be configurable to support a broad spectrum of devices and workloads. Leading vendors working in IAM for machine identities include Akeyless, Amazon Web Services (AWS), AppViewX, CyberArk, Delinea, Google, HashiCorp, Keyfactor, Microsoft, Venafi and others. 

For example, making identification and authorization of machine identities more intuitive to ensure keys and certificates are configured correctly is also needed. Securing machine identities as another threat surface is critical for protecting the devops process and machine–to–machine communications.  

Given how complex machine identities are to manage and secure, implementing least privileged access is challenging. There’s less control over workloads to limit the lateral movement of an attacker or the use of stolen certificates to launch malware attacks. What’s needed is the following:

  • Improved secrets management for every machine identity in a devops tool chain. Privileged access management (PAM) vendors are strengthening their support for machine identities and devops workflows, providing least privileged access support to the workload level.
  • Consolidate the variety of technologies to protect machine identities. Most machine identities are significantly different across departments, organizations, and divisions of companies. Their fragmented nature leads to a widening portfolio of technologies IT and cybersecurity teams need to manage and support. Those teams need a more consolidated view of the technologies that machine identities are built on and use, including Public Key Infrastructure (PKI) and other core technologies.  
  • IT and cybersecurity teams want to manage machine identities in hybrid and multicloud environments from a single dashboard. Vendors are committing to providing this, as enterprises clarify that this is one of their most crucial evaluation criteria. In addition, IT and cybersecurity teams are looking to reduce response times while streamlining reporting simultaneously.
  • Different teams across IT, devops, security and operations have entirely different needs regarding machine identity tools. The many differences in the tools, techniques and technologies each team requires for securing machine identities make implementing zero trust all the more challenging. There’s the baseline IAM system that every team relies on, and also the extensions each team needs to secure machine identities as work gets done. A cross-functional strategy is essential if an organization can develop a centralized governance approach. In addition, that is essential for achieving scale with IAM for machine identities.  

Knowing machine interdependence is key 

Using discovery methods and technologies first to locate then find interdependencies of machine identities must happen first. It’s a good idea to identify how machine identities vary in hybrid and multicloud environments, also tracking those with discovery tools. Finally, many CISOs realize that machine identities in multicloud environments need much more work to reduce the potential of being used to deliver malware or malicious executable code. Incorporating machine identities into a zero-trust framework needs to be an iterative process that can learn over time as the variety of workloads changes in response to new devops, IT, cybersecurity and broader cross-functional team needs.

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Thu, 28 Jul 2022 10:19:00 -0500 Louis Columbus en-US text/html
Killexams : CyberArk Software Ltd. (CYBR): How investors can get the most out of their investments

CyberArk Software Ltd. (CYBR) is priced at $128.97 after the most exact trading session. At the very opening of the session, the stock price was $132.58 and reached a high price of $132.74, prior to closing the session it reached the value of $131.13. The stock touched a low price of $125.54.Recently in News on July 22, 2022, CyberArk Named a Leader in 2022 Gartner Magic Quadrant for Privileged Access Management. CyberArk (NASDAQ: CYBR), the global leader in Identity Security, today announced it was named a Leader in the 2022 Gartner® Magic Quadrant™ for Privileged Access Management1. The company was positioned both highest in ability to execute and furthest in completeness of vision for the fourth time in a row. You can read further details here

CyberArk Software Ltd. had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the company’s stock is recorded $180.01 on 04/21/22, with the lowest value was $100.35 for the same time period, recorded on 05/12/22.

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CyberArk Software Ltd. (CYBR) full year performance was -9.39%

Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stock’s existing status and the future performance. Presently, CyberArk Software Ltd. shares are logging -36.05% during the 52-week period from high price, and 28.52% higher than the lowest price point for the same timeframe. The stock’s price range for the 52-week period managed to maintain the performance between $100.35 and $201.68.

The company’s shares, operating in the sector of Technology managed to top a trading volume set approximately around 616460 for the day, which was evidently higher, when compared to the average daily volumes of the shares.

When it comes to the year-to-date metrics, the CyberArk Software Ltd. (CYBR) recorded performance in the market was -25.57%, having the revenues showcasing -18.74% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 5.38B, as it employees total of 2140 workers.

Market experts do have their say about CyberArk Software Ltd. (CYBR)

During the last month, 0 analysts gave the CyberArk Software Ltd. a BUY rating, 0 of the polled analysts branded the stock as an OVERWEIGHT, 0 analysts were recommending to HOLD this stock, 0 of them gave the stock UNDERWEIGHT rating, and 0 of the polled analysts provided SELL rating.

According to the data provided on, the moving average of the company in the 100-day period was set at 145.58, with a change in the price was noted -36.69. In a similar fashion, CyberArk Software Ltd. posted a movement of -22.15% for the period of last 100 days, recording 401,501 in trading volumes.

Total Debt to Equity Ratio (D/E) can also provide valuable insight into the company’s financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders’ equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders’ equity The total Debt to Equity ratio for CYBR is recording 0.85 at the time of this writing. In addition, long term Debt to Equity ratio is set at 0.85.

Technical breakdown of CyberArk Software Ltd. (CYBR)

Raw Stochastic average of CyberArk Software Ltd. in the period of last 50 days is set at 41.42%. The result represents improvement in oppose to Raw Stochastic average for the period of the last 20 days, recording 26.81%. In the last 20 days, the company’s Stochastic %K was 43.00% and its Stochastic %D was recorded 60.76%.

Bearing in mind the latest performance of CyberArk Software Ltd., several moving trends are noted. Year-to-date Price performance of the company’s stock appears to be encouraging, given the fact the metric is recording -25.57%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -1.06%, alongside a downfall of -9.39% for the period of the last 12 months. The shares increased approximately by -8.21% in the 7-day charts and went down by -6.22% in the period of the last 30 days. Common stock shares were lifted by -18.74% during last recorded quarter.

Wed, 27 Jul 2022 23:33:00 -0500 en-US text/html
Killexams : Lavi Lazarovitz Lavi Lazarovitz leads a team of CyberArk Labs security researchers. He studies the methods and tactics used by attacker to penetrate and move laterally across organizational networks, and is responsible for devising effective detection and mitigation techniques to thwart these attacks. He previously served 11 years in the Israeli Air Force as a pilot and as an intelligence officer.

Wed, 22 Apr 2020 09:19:00 -0500 en text/html
Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Boost the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex,, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html
Killexams : Privileged Identity Management Market Size, Growth Projection, Latest Industry Trends, Market Share by Application and Regional Forecast 2022-2028

The MarketWatch News Department was not involved in the creation of this content.

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Mon, 11 Jul 2022 15:04:00 -0500 en-US text/html
Killexams : Internet Security Market Opportunities by Market Trends, Competitive landscaping, detailed strategies, financials, and exact developments

New Research Study “”Internet Security Market 2022 analysis by Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges and Investment Opportunities), Size, Share and Outlook“” has been added to Coherent Market insight

Global Internet Security Market 2022 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2020-2025.This research study of Internet Security  market involved the extensive usage of both primary and secondary data sources. This includes the study of various parameters affecting the industry, including the government policy, market environment, competitive landscape, historical data, present trends in the market, technological innovation, upcoming technologies and the technical progress in related industry.

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The Internet Security market is divided between organised and unorganised companies. The unorganised market now dominates the Internet Security market. However, over the predicted period of 2022-2028, this picture is expected to alter. Lifestyle Modification, Rising Due to urbanisation, Growing Middle Class Population, Local Availability and Availability of Snacks in Small Package Size, Low Price, and Company’s Strategies to Focus on Regional Taste are all contributing to the growth of the Internet Security Market.

Major Key players in this Market:

◘ IBM Corporation
◘ Hewlett Packard Enterprise
◘ McAfee LLC
◘ Trend Micro Inc.
◘ Symantec Corporation
◘ Cisco Systems Inc.
◘ Palo Alto Networks Inc.
◘ Dell EMC
◘ Fortinet Inc.
◘ Sophos Ltd.
◘ Rapid7 Inc.
◘ CyberArk Software Ltd.
◘ Splunk Inc.
◘ Imperva Inc.

Drivers & Trends

The Internet Security Market is reliant on a number of factors that can either help or hinder the industry overall. The variables are presented and classified according to their potential impact on the Internet Security Market. Various factors are defined in the report for all of the Internet Security Market segments and countries. These variables have data attached to them.

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Global Internet Security Market Taxonomy

Internet security market is segmented on the basis of solutions, services, security type, deployment mode, organization size, application, and region

On the basis of solution

  • Data Loss Prevention (DLP)
  • Distributed Denial of Service Mitigation
  • Intrusion Detection System/Intrusion Prevention System (Ids/Ips)
  • Firewall
  • Security and Vulnerability Management
  • Anti-virus/Anti malware
  • Identity and Access Management (IAM)
  • Encryption
  • Security Information and Event Management
  • Disaster Recovery
  • Unified Threat Management
  • Risk and Compliance Management

On the basis of services

  • Professional Services
  • Design and Integration
  • Risk and Threat Assessment
  • Consulting
  • Support and Maintenance
  • Training and Education
  • Managed Services

On the basis of security type

  • Application Security
  • Network Security
  • Content Security
  • Wireless Security
  • Cloud Security
  • Endpoint Security

On the basis of deployment mode

On the basis of organization size

  • Large Enterprises
  • Small and Medium-sized Enterprises

On the basis of application

  • Manufacturing
  • Government
  • Energy and Utilities
  • Healthcare
  • Aerospace and Defense
  • Retail
  • Banking, Financial Services and Insurance (BFSI)
  • IT and Telecom
  • Public Utilities
  • Military

Regional Outlook:

The Asia Pacific, North America, Europe, Latin America, and the Rest of the World are examined in the geographical analysis of the worldwide Internet Security market. Because of its well-established ICT service providers and big consumer base, North America is the world’s leading/significant area in terms of market share. Over the projected period 2022-2028, Asia-Pacific is expected to have the greatest growth rate / CAGR.

Method of Research

For the time frame 2022-2028, the market research team used Porter’s Five Force Model to examine the Global Internet Security Market demand. In addition, a thorough SWOT analysis is carried out to help the reader make more informed decisions about the Global Internet Security Market demand. We used both primary and secondary data collection techniques. In addition, for a thorough analysis of the market, the data analysts used publicly available tools such as annual accounts, SEC filings, and white papers. The approach to analysis clearly reflects the goal of having it evaluated against various metrics in order to provide a comprehensive view of the market.

Report Includes:

  • An up-to-date detailed analysis of the global markets for Internet Security .
  • Analyses of global market trends, including data from 2018 and 2021, predictions for 2022 and 2024, and compound annual growth rates (CAGRs) through 2028.
  • The worldwide Internet Security market size is estimated and forecasted, with market share analysis by Internet Security type, component, application, end-user industry, and geographic area.
  • Highlights of the industry’s market potential for Internet Security , emerging applications, technological advancements, and strategic innovations
  • COVID-19 consequences on market advancement and assessment of feasible technological drivers through a comprehensive examination of numerous Internet Security specialised applications for new and existing sub-parts.
  • Recent industry structure, present competitive landscape, R&D activities, significant growth initiatives, and business value share analysis based on segmental sales are all included.
  • Review of patents granted for Internet Security , and assessment of new developments within the industry, as well as new advances in the sector.
  • Company profiles of the the world’s leading global players are IBM Corporation, Hewlett Packard Enterprise, McAfee LLC, Trend Micro, Inc., Symantec Corporation, Cisco Systems, Inc., Palo Alto Networks, Inc., Dell EMC, Fortinet, Inc., Sophos Ltd., Rapid7, Inc., CyberArk Software Ltd., Splunk, Inc., and Imperva, Inc.

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Table of Contents with Major Points:

1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2018-2028 (USD Billion)
1.2.1. Internet Security  Market, by Region, 2018-2028 (USD Billion)
1.2.2. Internet Security  Market, by Type, 2018-2028 (USD Billion)
1.2.3. Internet Security  Market, by Application, 2018-2028 (USD Billion)
1.2.4. Internet Security  Market, by Verticles, 2018-2028 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption

2. Global Internet Security  Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates

3. Global Internet Security  Market Dynamics
3.1. Internet Security  Market Impact Analysis (2018-2028)
3.1.1. Market Drivers
3.1.2. Market Challenges
3.1.3. Market Opportunities

4. Global Internet Security  Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2028)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion

5. Global Internet Security  Market, by Type
5.1. Market Snapshot
5.2. Global Internet Security  Market by Type, Performance – Potential Analysis
5.3. Global Internet Security  Market Estimates & Forecasts by Type 2018-2028 (USD Billion)
5.4. Internet Security  Market, Sub Segment Analysis

6. Global Internet Security  Market, by Application
6.1. Market Snapshot
6.2. Global Internet Security  Market by Application, Performance – Potential Analysis
6.3. Global Internet Security  Market Estimates & Forecasts by Application 2018-2028 (USD Billion)
6.4. Internet Security  Market, Sub Segment Analysis
6.4.1. Others

7. Global Internet Security  Market, by Verticles
7.1. Market Snapshot
7.2. Global Internet Security  Market by Verticles, Performance – Potential Analysis
7.3. Global Internet Security  Market Estimates & Forecasts by Verticles 2018-2028 (USD Billion)
7.4. Internet Security  Market, Sub Segment Analysis

8. Global Internet Security  Market, Regional Analysis
8.1. Internet Security  Market, Regional Market Snapshot
8.2. North America Internet Security  Market
8.3. Europe Internet Security  Market Snapshot
8.4. Asia-Pacific Internet Security  Market Snapshot
8.5. Latin America Internet Security  Market Snapshot
8.6. Rest of The World Internet Security  Market

9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. Keyplayer1 Key InDurationation Overview Financial (Subject to Data Availability) Product Summary exact Developments

10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes


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