The Top Ten AI Use Cases in Enterprise Cybersecurity

Cybersecurity has entered an era where machine learning plays a pivotal role in responding to, preventing, and predicting cyberattacks. A diverse range of machine learning techniques, including Regression, Classification, Clustering, Association Rules, Dimensionality Reduction, and Generative Modelling, are being harnessed, tailored to specific use cases. Drawing from my extensive experience, I have curated a list of ten exceptional use cases in this critical field.

  1. Windows User Login Anomaly Detection: Leveraging the Windows event logger, which meticulously records every system event, offers a means to detect anomalies in user logins. By correlating event code 4624, representing login events, with user activities, clustering techniques can identify irregular login patterns.
  2. Risk Identification on IT Assets: Ensuring compliance with organizational policies is of paramount importance. Detecting risks associated with non-compliant assets is critical, as non-compliance can create vulnerabilities to cyberattacks. Utilizing regression techniques to predict potential non-compliance events, such as outdated antivirus signatures due to infrequent laptop connectivity, enables proactive actions to safeguard against security breaches.
  3. API Monitoring: In an evolving API-driven landscape, monitoring both consumed and exposed APIs is imperative. Transforming API data into a model and applying dimensionality reduction techniques can identify anomalies, thereby ensuring the security of interdependent APIs.
  4. System Incident and Event Management (SIEM): Leading SIEM solutions like IBM QRadar and Splunk integrate AI and ML models to detect anomalies in system events. Creating situational event models from SIEM-generated data allows for proactive event detection, a crucial approach in a cybersecurity landscape where businesses invest substantially.
  5. Malware Detection and Classification: Real-time identification of malware is a linchpin of cybersecurity. Clustering algorithms followed by classification are instrumental in monitoring endpoints for unusual behaviors, such as new processes listening on unused ports or unexpected registry changes.
  6. Network Log Analysis: Analyzing network and application log files plays a pivotal role in identifying intrusions. Machine learning models designed for network security, including DNS tunnel attack detection and network intrusion detection, facilitate the recognition of deviations from trained models, a critical defense against zero-day attacks.
  7. User and Entity Behavioral Analysis (UEBA): Tracking user and entity behavior is paramount for anomaly detection. Alterations in user login locations, access patterns, or privileges can signal potential threats. Translating this data into a model allows for the detection of anomalies.
  8. AI-Based Threat Mitigation: Cyber attackers increasingly employ machine learning to exploit vulnerabilities. To counter this, harnessing all available data on IT assets and users to construct real-time models that pinpoint vulnerabilities is indispensable.
  9. AI-Enabled Penetration Testing: AI-enhanced penetration testing prioritizes vulnerabilities and predicts remediation steps, substantially enhancing the effectiveness of security administrators.
  10. Deception Using Honeypots: Implementing deception-centered honeypot models aids in comprehending hacker intent. Tracking hacker activities and constructing data models based on their actions yield invaluable insights.

Machine learning algorithms serve as a powerful arsenal to address a wide spectrum of cybersecurity challenges, empowering organizations to fortify their defenses against ever-evolving threats.

About Author

Ram is a Cloud Security Expert with 30+ years of IT experience, holding 26 patents in Infra, AI-ML, and Automation. He’s a Wipro Fellow, an Independent Consultant for Fortune 15 companies, and has won international awards for Automation. Ram’s cost rationalization work benefited enterprises like Citi Bank, Credit Suisse, and UBS.

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