Description

This curriculum spans the equivalent of a multi-workshop technical advisory engagement, addressing AI security across the SOC lifecycle—from threat modeling and pipeline controls to incident response and third-party risk—mirroring the depth required in enterprise programs that operationalize secure AI at scale.

Module 1: Threat Landscape and AI-Specific Attack Vectors

Selecting which adversarial attack types (e.g., evasion, poisoning, model inversion) to prioritize based on deployed AI use cases in the SOC.
Integrating threat intelligence feeds that specifically track AI model exploits and machine learning supply chain vulnerabilities.
Mapping MITRE ATLAS tactics to existing SOC detection rules to identify coverage gaps for AI-targeted attacks.
Assessing risks associated with third-party AI models used in threat detection, including undocumented training data biases.
Defining thresholds for model confidence score anomalies that trigger incident response workflows.
Implementing logging mechanisms to capture model input-output pairs for forensic reconstruction during compromise investigations.

Module 2: Securing AI Development and Deployment Pipelines

Enforcing code signing and artifact provenance checks in MLOps pipelines to prevent tampering with model binaries.
Configuring isolated staging environments where new models undergo security validation before SOC production deployment.
Implementing static analysis tools to detect hardcoded credentials or insecure API calls in data preprocessing scripts.
Requiring peer review of training data sourcing procedures to mitigate data poisoning risks.
Integrating automated scanning for known vulnerable dependencies in Python packages used in model training.
Establishing rollback procedures for AI models when post-deployment integrity checks fail.

Module 3: Model Integrity and Adversarial Robustness Testing

Designing red team exercises that simulate data poisoning during model retraining cycles using historical telemetry.
Generating adversarial samples to evaluate detection model resilience without disrupting live SOC operations.
Selecting appropriate perturbation bounds for evasion testing based on realistic attacker capabilities.
Implementing runtime checks for out-of-distribution inputs that may indicate evasion attempts.
Calibrating model monitoring alerts to distinguish between concept drift and deliberate manipulation.
Documenting model decision boundaries for high-risk detection rules to support audit and incident analysis.

Module 4: Secure Integration of AI into SOC Workflows

Configuring role-based access controls to limit who can modify or override AI-generated alerts in the SIEM.
Designing human-in-the-loop approval steps for AI-recommended containment actions like firewall blockings.
Implementing audit trails that record when and why analysts accept or reject AI-generated incident hypotheses.
Integrating AI confidence scores into ticket prioritization without creating alert fatigue from low-certainty predictions.
Validating that AI tools do not inadvertently expose PII or regulated data during log summarization tasks.
Ensuring failover mechanisms route alerts to human analysts when AI subsystems experience outages.

Module 5: Data Security and Privacy in AI Systems

Applying differential privacy techniques during model training when using sensitive network flow data.
Implementing data retention policies for training datasets that align with regulatory requirements and breach exposure risks.
Encrypting model feature stores at rest and in transit, particularly when shared across SOC teams.
Conducting data lineage audits to verify that no unauthorized data sources were used in model training.
Masking or tokenizing user identifiers in logs before they are fed into AI-driven behavioral analytics models.
Assessing privacy risks of model inversion attacks that could reconstruct raw logs from model outputs.

Module 6: Monitoring, Logging, and Incident Response for AI Components

Deploying dedicated log collectors for AI inference endpoints to capture model performance and input anomalies.
Correlating model degradation signals (e.g., accuracy drops) with concurrent infrastructure or data changes.
Creating playbooks for responding to confirmed model poisoning incidents, including data quarantine procedures.
Instrumenting models to emit structured telemetry for integration with existing SOC incident management systems.
Setting up anomaly detection on model update frequency to detect unauthorized retraining attempts.
Conducting post-incident reviews that evaluate whether AI components contributed to detection or response delays.

Module 7: Governance, Compliance, and Model Lifecycle Management

Establishing model inventory registers that track version, owner, training data source, and risk classification.
Defining retention periods for model artifacts to support forensic investigations and regulatory audits.
Requiring security risk assessments before deploying AI models that influence critical SOC decisions.
Implementing model deprecation workflows that include notification to dependent teams and traffic rerouting.
Aligning AI model documentation with NIST AI RMF and ISO/IEC 42001 requirements for certification readiness.
Conducting periodic reassessment of model fairness metrics to prevent discriminatory alerting patterns.

Module 8: Third-Party Risk and Supply Chain Security for AI Tools

Requiring software bills of materials (SBOMs) from vendors providing AI-powered threat detection platforms.
Auditing third-party model training practices through contractual security clauses and right-to-audit provisions.
Isolating vendor-hosted AI inference APIs using reverse proxies with traffic inspection and rate limiting.
Evaluating the security posture of open-source AI frameworks before adoption in SOC tooling.
Monitoring for unauthorized model exfiltration via API responses or logging endpoints.
Developing contingency plans for replacing cloud-based AI services that experience prolonged outages or breaches.