AI-Driven Detection Tools

Purpose and Scope

AI and machine learning are increasingly used in security tools for anomaly detection, threat classification, and automated analysis. This playbook covers how to effectively work with AI-driven detection tools, understand their outputs, and integrate them into SOC workflows.

Prerequisites

• AI-enabled tools: SIEM with ML capabilities, UEBA platform, or dedicated AI security tools
• Baseline data: Historical data for model training
• Understanding of tool capabilities: What the AI can and cannot detect
• Feedback mechanisms: Ability to provide input to improve models

Types of AI in Security

Anomaly Detection

ML models that learn normal patterns and flag deviations:

• User behavior analytics (unusual login times, locations, actions)
• Network traffic anomalies (unexpected connections, volumes)
• System behavior anomalies (unusual processes, resource usage)

Classification

Models that categorize inputs into threat categories:

• Malware classification (family, behavior type)
• Phishing detection (email and URL classification)
• Alert prioritization (risk scoring)

Natural Language Processing

AI that processes text-based security data:

• Log summarization and analysis
• Threat intelligence extraction
• Incident report generation

Automated Investigation

AI that assists with or automates investigation tasks:

• Alert enrichment and correlation
• Root cause analysis suggestions
• Response recommendation

Understanding AI Outputs

Confidence Scores

Most AI tools provide confidence or risk scores:

• Scores indicate model certainty, not absolute truth
• High confidence does not guarantee accuracy
• Thresholds determine when alerts are generated
• Understand what factors contribute to the score

Explainability

Look for tools that explain their decisions:

• Which features contributed to the detection
• What baseline was compared against
• Similar historical events or patterns
• Reasoning chain for classification decisions

Uncertainty

AI models have inherent limitations:

• Novel attacks may not match learned patterns
• Adversaries can attempt to evade ML detection
• Model drift over time as environments change
• Bias in training data affects detection

Integrating AI into Workflows

Triage Assistance

Use AI to prioritize and enrich alerts:

• Risk scores help prioritize the queue
• Automated enrichment adds context
• Correlation groups related alerts
• Classification suggests alert category

Investigation Support

AI can accelerate investigation:

• Suggested investigation steps
• Automated data gathering
• Timeline reconstruction
• Similar incident identification

Human Oversight

Maintain human decision-making for critical actions:

• AI recommends, humans decide on containment
• Review AI conclusions before escalation
• Validate automated responses periodically
• Maintain skills for AI-independent investigation

Training and Tuning

Baseline Period

AI models need time to learn normal behavior:

• Typical baseline periods range from 14 to 90 days
• Baselines should include representative activity
• Avoid training during unusual periods (holidays, outages)
• Multiple baselines may be needed (weekday vs. weekend)

Feedback Loops

Improve models by providing feedback:

• Mark false positives to reduce similar alerts
• Confirm true positives to reinforce detection
• Report missed detections when discovered
• Provide context on legitimate behavior changes

Model Updates

Keep AI models current:

• Retrain periodically with new data
• Update when environment changes significantly
• Apply vendor model updates promptly
• Monitor for model degradation

Threshold Management

Balancing Sensitivity

• Lower thresholds catch more threats but increase false positives
• Higher thresholds reduce noise but may miss threats
• Different thresholds for different risk levels (crown jewels vs. general assets)
• Adjust based on operational capacity

Threshold Tuning Process

1. Start with vendor recommended settings
2. Monitor false positive and detection rates
3. Adjust incrementally based on data
4. Document threshold decisions and rationale
5. Review and adjust periodically

Challenges and Limitations

Adversarial Evasion

Attackers may try to evade AI detection:

• Mimicking normal behavior patterns
• Slow and low attacks below thresholds
• Poisoning training data
• Exploiting model blind spots

Data Quality Issues

• Incomplete data leads to poor detection
• Mislabeled training data degrades accuracy
• Data drift when collection changes
• Bias from non-representative samples

Operational Challenges

• Black box models difficult to explain to stakeholders
• Over-reliance on AI reduces analyst skills
• Alert fatigue from poorly tuned models
• Vendor lock-in for proprietary models

Best Practices

• Layer defenses: Use AI alongside signature-based detection
• Understand the model: Know what it detects and its limitations
• Maintain human oversight: AI assists but does not replace analysts
• Provide feedback: Actively improve models through feedback
• Monitor performance: Track detection rates and false positives
• Plan for failure: Have procedures when AI is unavailable or wrong
• Stay current: Keep models and threat intelligence updated

Evaluating AI Tools

When selecting AI-driven security tools, consider:

• Explainability: Can you understand why it flagged something?
• Customization: Can you tune models for your environment?
• Feedback integration: Does analyst input improve the model?
• Performance metrics: What are documented detection and false positive rates?
• Data requirements: What data does it need and how much?
• Integration: Does it work with your existing tools?

References

• MITRE ATLAS (Adversarial Threat Landscape for AI Systems): atlas.mitre.org
• NIST AI Risk Management Framework
• Gartner AI in Security Operations research
• Microsoft Sentinel ML documentation: Microsoft documentation