Machine Learning: A Stepping Stone to AI-Led Security

The digital transformation of security is happening now. IoT, cloud, and mobile technologies are providing new solutions for our security challenges; they also add to the already very complex security landscape. New attack vectors, social engineering, and malicious technologies are rapidly increasing in sophistication and approach. How do we stay ahead of the curve, combining robust preventative methods with proactive identity and access management to ensure our workplace and data are protected?

Machine learning and artificial intelligence for both physical and cybersecurity are here to help. Digital security transformation tools like HID SAFE™ use advanced AI algorithms to simplify security by automatically detecting and preventing possible fraudulent behavior and workplace access.

Below we outline nine insights into how machine learning and AI can help streamline and enhance your security operations.

1. Proactive Security Helps IT Work in Partnership With the Business

IT security needs to partner fully and deeply with your strategic business goals. This means moving to a proactive approach that takes advantage of automation that leverages data and analytics capabilities. Real-time security analytics, enabled by machine learning and artificial intelligence, allows organizations to act on meaningful, contextual insights from their security data. This starts by securing physical access.

How Machine Learning Enhances Physical Security and Life SafetyDiagram explaining machine learningHow Machine Learning Enhances Physical Security and Life SafetyDatafed inUserbehaviorPhysical identityand accessmanagement dataIdentity informationBadge informationand swipe logsAccess assignmentsReader-site-accessarea hierarchies

2. Machine Learning for Physical Security and Safety

Machine learning uses a combination of existing data, known baselines, user behavior, and Identity and Access Management (IAM) information to manage:

  • Identity information
  • Badge information and audit trails
  • Access levels and assignments
  • Site access and hierarchies

That’s not enough by itself, but machine learning can quickly identify unusual behaviors and access attempts.

3. Automatically Creating Machine Learning Rules for User Behavior

One of the most powerful aspects of the AI algorithm is to identify what is “expected” of users. For example, where, when, and on what device does an employee typically log in? If they access the system in a different way, should they be challenged to provide additional authentication? AI addresses some of these issues through risk factors.

4. Risk Factors and Knowledge Bases

As machine learning understands your specific business, it can build up an internal knowledge base around data sensitivity and role-based access. This can be combined with risk analysis to request additional authorization for unusual access requests.

Machine Learning Cybersecurity ComponentsMachine Learning Cybersecurity Components InfographicMachine Learning Cybersecurity Components NetworkmonitoringPACS andPIAM dataAccess management solutions• Advanced Access Manager• Credential Manager• Visitor ManagerSecurity reportsand operationalanalytics

5. Machine Learning Cybersecurity Components

Four essential areas driving the effectiveness of machine learning and AI for network security include:

  • Network monitoring to identify unexpected or unusual access attempts
  • Access management solutions that combine data from multiple systems to form a complete view of visitors and employees
  • Picture Archiving and Communications Systems (PACS) and Physical Identity and Access Management integration
  • Security reports and operational analytics

Of course, all of this needs to be supported with a robust, predictive strategy.

6. Predictive Strategy Answers Several Important Questions

A smart predictive security strategy helps answer the following questions:

  • What is the automated response to tackle the threat?
  • Which assets are most vulnerable and likely to be targeted?
  • What is the source of the next threat?
  • Which processes need improvement?

Answering these questions requires commitment and investment in cybersecurity platforms.

Security Spending — Automation AwarenessGrows, but Slowed by Navigating Past HurdlesSecurity Spending InfographicSecurity Spending — Automation AwarenessGrows, but Slowed by Navigating Past Hurdles8.7% this year, up to $124 billion.Top security investments1 Worldwide spending will jump by#3Artificial intelligence#4Big data#6Real-time analytics

7. Investment in Automation for Machine Learning and Cybersecurity is Growing

Artificial intelligence, big data, and real-time analytics are vital areas for cybersecurity investment. Worldwide spending on cybersecurity as a whole is expected to grow by almost nine percent this year, to a total of over $120 billion. Despite this, there are still hurdles.

Growth and awareness of risk analyticsStats on Growth and Awareness of Risk AnalyticsGrowth and Awareness of Risk Analyticssaid that automation, machine learning, artificialintelligence, andorchestration strengthencyberresilience.3reported moderate to significant use of AIand ML forcybersecurity.4are actively evaluatingpredictive analyticssolutions.560%71%46%But 52% saidpredictive analyticsisn’t a budget priority,despite recognizingthe need for it.635% haveno plans toemploy predictiveanalytics.7 Only 17% saidbig data analyticsfor security is apotential newinvestment area.8

8. Hurdles to Implementing Effective Machine Learning for Cybersecurity

Surveys of cybersecurity teams and managers have uncovered some surprising facts about AI implementation:

  • 60% say that automation and machine learning strengthen cyber resilience
  • 71% say that their organization uses AI and ML moderately or significantly for cybersecurity
  • 46% are looking at predictive analytics solutions
  • More than half say predictive analytics is not a priority in their budgets
  • Over a third say that there are no plans to use predictive analytics
  • Only 17% said big data analytics for security is a potential investment area

The ChallengeInfographic - Challenges to Implementing AIThe ChallengeMore investment needed:Only 30%What’s holding back adoption?Slow start on data captureLack of quality dataLack of expertise and understandingPerception that it’s anall-or-nothing propositionof organizations plan to increase security riskanalytics spending.8Leading to adoption missteps and use of AI solutions that don’tsolve the problem at hand

A slow start in data capture, a lack of quality data, a lack of expertise, and aversion to an “all or nothing” approach are all blockages to implementing proactive, predictive machine learning into cybersecurity solutions.


Benefits of Implementing Machine LearningInfographic - Benefits of Implementing Machine LearningBenefitsIncreasedefficiencyAgilityImprovedbusinessalignmentReliabilityCost savingsCompoundedlearning thatbecomes morevaluable over time

9. Benefits of ML and AI are Significant

For the organizations that can overcome these issues, there are large benefits to be had:

  • Increased efficiency for security rules, automation, and access management
  • Greater agility to react to changes in business strategies, security approaches, and other areas
  • Improved alignment with business strategies and intentions
  • Strong reliability, meaning less manual effort and lower costs
  • Compounded learning and knowledge sharing
  • Fewer vulnerabilities and fewer chances of data breaches or illegal access

If you’re interested in using machine learning to gain a better insight into your workplace’s physical security operations, you should start collecting information now even if you’re not yet ready to apply analytics or machine learning.

Want to learn more about HID SAFE’s AI capabilities? Download our white paper to learn how security professionals are using predictive analytics to prevent threats to their organizations.

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Ian Lowe is Product Marketing Director for HID SAFE and is passionate about marketing all things related to Identity, Cybersecurity, IoT, Cloud and Digital transformation. In his 19-year career, Ian has become a recognized product marketing and sales enablement leader having created and launched successful cloud-based identity and security solutions that are used by top technology firms, financial services organizations and governments around the world today.

Source:
1 Altimeter’s recent “State of Digital Transformation” survey.
2 “2019 IDG Security Priorities” study.
3 Altimeter op. cit.
4 Ponemon Institute, “The Cyber Resilient Organization,” 2019.
5 Security Analytics Market Research, HID SAFE, 2018.
6 Ibid.
7 IDG op. cit.
8 Ibid.