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Three Ways Machine Learning Is Revolutionizing Zero Trust Security

Bottom Line: Zero Trust Security (ZTS) starts with Next-Gen Access (NGA). Capitalizing on machine learning technology to enable NGA is essential in achieving user adoption, scalability, and agility in securing applications, devices, endpoints, and infrastructure.

How Next-Gen Access and Machine Learning Enable Zero Trust Security

Zero Trust Security provides digital businesses with the security strategy they need to keep growing by scaling across each new perimeter and endpoint created as a result of growth. ZTS in the context of Next-Gen Access is built on four main pillars: (1) verify the user, (2) validate their device, (3) limit access and privilege, and (4) learn and adapt. The fourth pillar heavily relies on machine learning to discover risky user behavior and apply for conditional access without impacting user experience by looking for contextual and behavior patterns in access data.

As ZTS assumes that untrusted users or actors already exist both inside and outside the network, machine learning provides NGA with the capability to assess data about users, their devices, and behavior to allow access, block access, or enforce additional authentication. With machine learning, policies and user profiles can be adjusted automatically and in real-time. While NGA enabled by machine learning is delivering dashboards and alerts, the real-time response to security threats predicated on risk scores is very effective in thwarting breaches before they start.

Building NGA apps based on machine learning technology yields the benefits of being non-intrusive, supporting the productivity of workforce and business partners, and ultimately allowing digital businesses to grow without interruption. For example, Centrify’s rapid advances in machine learning and Next-Gen Access to enable ZTS strategies makes this company one of the most interesting to watch in enterprise security.

The following are three ways machine learning is revolutionizing Zero Trust Security:

  1. Machine learning enables enterprises to adopt a risk-based security strategy that can flex with their business as it grows. Many digital businesses have realized that “risk is security’s new compliance,” and therefore are implementing a risk-driven rather than a compliance-driven approach. Relying on machine learning technology to assess user, device, and behavioral data for each access request derives a real-time risk score. This risk score can then be used to determine whether to allow access, block access, or step up authentication. In evaluating each access request, machine learning engines process multiple factors, including the location of the access attempt, browser type, operating system, endpoint device status, user attributes, time of day, and unusual recent privilege change. Machine learning algorithms are also scaling to take into account unusual command runs, unusual resource access histories, and any unusual accounts used, unusual privileges requested and used, and more. This approach helps thwart comprised credential attacks, which make up 81% of all hacking-related data breaches, according to Verizon.
  2. Machine learning makes it possible to accomplish security policy alignment at scale. To keep pace with a growing digital business’ need to flex and scale to support new business models, machine learning also assists in automatically adjusting user profiles and access policies based on behavioral patterns. By doing so, the need for IT staffers to review and adjust policies vanishes, freeing them up to focus on things that will grow the business faster and more profitably. On the other hand, end users are not burdened with step-up authentication once a prior abnormal behavior is identified as now typical behavior and therefore both user profile and policies updated.
  3. Machine learning brings greater contextual intelligence into authentication, streamlining the experience and increasing user adoption. Ultimately, the best security is transparent and non-intrusive. That’s where the use of risk-based authentication and machine learning technology comes into play. The main impediment to adoption for multi-factor authentication has been the perceived impact on the productivity and agility of end users. A recent study by Dow Jones Customer Intelligence and Centrify revealed that 62% of CEOs state that multi-factor authentication (MFA) is difficult to manage and is not user-friendly, while only 41% of technical officers (CIOs, CTOs, and CISOs) agree with this assessment. For example, having to manually type in a code that has been transmitted via SMS in addition to the already supplied username and password is often seen as cumbersome. Technology advancements are removing some of these objections by offering a more user-friendly experience, like eliminating the need to manually enter a one-time password on the endpoint, by enabling the user to simply click a button on their smartphone. Nonetheless, some users still express frustration with this additional step, even if it is relatively quick and simple. To overcome these remaining barriers to adoption, machine learning technology contributes to minimizing the exposure to step up authentication over time, as the engine learns and adapts to the behavioral patterns.

In Conclusion

Zero Trust Security through the power of Next-Gen Access is allowing digital businesses to continue on their path of growth while safeguarding their patented ideas and intellectual property. Relying on machine learning technology for Next-Gen Access results in real-time security, allowing to identify high-risk events and ultimately greatly minimizing the effort required to identify threats across today’s hybrid IT environment.

The Best Big Data Companies And CEOs To Work For In 2018

Forbes readers’ most common requests center on who the best companies are to work for in analytics, big data, data management, data science and machine learning. The latest Computer Reseller News‘ 2018 Big Data 100 list of companies is used to complete the analysis as it is an impartial, independent list aggregated based on CRN’s analysis and perspectives of the market. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Using the 2018 Big Data 100 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the following analysis was completed today. 25 companies on the list have very few (less than 15) or no Glassdoor reviews, so they are excluded from the rankings. Based on analysis of Glassdoor score patterns over the last four years, the lower the number of rankings, the more 100% scores for referrals and CEOs. These companies, however, are included in the full data set available here. If the image below is not visible in your browser, you can view the rankings here.

 

The highest rated CEOs on Glassdoor as of May 11, 2018 include the following:

Dataiku Florian Douetteau 100%
StreamSets Girish Pancha 100%
MemSQL Nikita Shamgunov 100%
1010 Data Greg Munves 99%
Salesforce.com Marc Benioff 98%
Attivio Stephen Baker 98%
SAP Bill McDermott 97%
Qubole Ashish Thusoo 97%
Trifacta Adam Wilson 97%
Zaloni Ben Sharma 97%
Reltio Manish Sood 96%
Microsoft Satya Nadella 96%
Cloudera Thomas J. Reilly 96%
Sumo Logic Ramin Sayar 96%
Google Sundar Pichai 95%
Looker Frank Bien 93%
MongoDB Dev Ittycheria 92%
Snowflake Computing Bob Muglia 92%
Talend Mike Tuchen 92%
Databricks Ali Ghodsi 90%
Informatica Anil Chakravarthy 90%

 

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

  • Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
  • It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
  • Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look Beyond Resumes To Win The War For Talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

  • Career Growth Bell Curve – Illustrates how a given candidate’s career progressions and performance compares relative to others.

  • Social Following On Public Sites –  Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

  • Highlights Of Background That Is Relevant To Job(s) Under Review Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

  • Recent Publications – Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

  • Professional overlap that makes it easier to validate achievements chronicled in the resume – Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

  1. Resumes are like rearview mirrors that primarily reflect the past. What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.
  2. By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in. The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.
  3. It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue. Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.
  4. It’s time to take a more data-driven approach to diversity that removes unconscious biases. Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.
  5. Reduces the risk of making a bad hire that will churn out of the organization fast. Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

 

How Zero Trust Security Fuels New Business Growth

Bottom Line: Zero Trust Security (ZTS) strategies enabled by Next-Gen Access (NGA) are indispensable for assuring uninterrupted digital business growth, and are proving to be a scalable security framework for streamlining onboarding and systems access for sales channels, partners, patients, and customers of fast-growing businesses.

The era of Zero Trust Security is here, accelerated by NGA solutions and driven by the needs of digital businesses for security strategies that can keep up with the rapidly expanding perimeters of their businesses. Internet of Things (IoT) networks and the sensors that comprise them are proliferating network endpoints and extending the perimeters of growing businesses quickly.

Inherent in the DNA of Next-Gen Access is the ability to verify the user, validate the device (including any sensor connected to an IoT network), limit access and privilege, then learn and adapt using machine learning techniques to streamline the user experience while granting access to approved accounts and resources. Many digital businesses today rely on IoT-based networks to connect with suppliers, channels, service providers and customers and gain valuable data they use to grow their businesses. Next-Gen Access solutions including those from Centrify are enabling Zero Trust Security strategies that scale to secure the perimeters of growing businesses without interrupting growth.

How Zero Trust Security Fuels New Business Growth  

The greater the complexity, scale and growth potential of any new digital business, the more critical NGA becomes for enabling ZTS to scale and protect its expanding perimeters. One of the most valuable ways NGA enables ZTS is using machine learning to learn and adapt to users’ system access behaviors continuously. Insights gained from NGA strengthen ZTS frameworks, enabling them to make the following contributions to new business growth:

  1. Zero Trust Security prevents data breaches that cripple new digital business models and ventures just beginning to scale and grow. Verifying, validating, learning and adapting to every user’s access attempts and then quantifying their behavior in a risk score is at the core of Next-Gen Access’ DNA. The risk scores quantify the relative levels of trust for each system user and determine what, if any, additional authentication is needed before access is granted to requested resources. Risk scores are continuously updated with every access attempt, making authentication less intrusive over time while greatly reducing compromised credential attacks.
  2. Securing the expanding endpoints and perimeters of a digital business using NGA frees IT and senior management up to focus more on growing the business. In any growing digital business, there’s an exponential increase in the number of endpoints being created, rapidly expanding the global perimeter of the business. The greater the number of endpoints and the broader the perimeter, the more revenue potential there is. Relying on Next-Gen Access to scale ZTS across all endpoints saves valuable IT time that can be dedicated to direct revenue-producing projects and initiatives. And by relying on NGA as the trust engine that enables ZTS, senior management will have far fewer security-related emergencies, interruptions, and special projects and can dedicate more time to growing the business. A ZTS framework also centralizes security management across a digital business, alleviating the costly, time-consuming task of continually installing patches and updates.
  3. Zero Trust Security is enabling digital businesses globally to meet and exceed General Data Protection Regulation (GDPR) compliance requirements while protecting and growing their most valuable asset: customer trust. Every week brings new announcements of security breaches at many of the world’s most well-known companies. Quick stats on users affected, potential dollar loss to the company and the all-too-common 800 numbers for credit bureaus seem to be in every press release. What’s missing is the incalculable, unquantifiable cost of lost customer value and the millions of hours customers waste trying to avert financial chaos. In response to the need for greater oversight of how organizations respond to breaches and manage data security, the European Union (EU) launched General Data Protection Regulation (GDPR) which goes into effect May 25, 2018. GDPR applies not only European organizations, but also to foreign businesses that offer goods or services in the European Union (EU) or monitor the behavior of individuals in the EU. The compliance directive also states that organizations need to process data so in a way that “ensures appropriate security of the personal data, using appropriate technical and organizational measures,” taking into account “state of the art and the costs of implementation.”

Using an NGA approach that includes risk-based multi-factor authentication (MFA) to evaluate every login combined with the least privilege approach across an entire organization is a first step towards excelling at GDPR compliance. Zero Trust Security provides every organization needing to comply with GDPR a solid roadmap of how to meet and exceed the initiative’s requirements and grow customer trust as a result.

Conclusion

Next-Gen Access enables Zero Trust Security strategies to scale and flex as a growing business expands. In the fastest growing businesses, endpoints are proliferating as new customers are gained, and suppliers are brought onboard. NGA ensures growth continues uninterrupted, helping to thwart comprised credential attacks, which make up 81% of all hacking-related data breaches, according to Verizon.

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