AI/ML Product Security Audit

 

An AI/ML Product Security Audit ensures that artificial intelligence and machine learning systems are developed, deployed, and maintained securely. It focuses not only on traditional application and infrastructure security but also on unique AI/ML risks like model poisoning, data leakage, adversarial inputs, and ethical concerns such as bias and explainability.

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📋 AI/ML Product Security Audit – Table Format

#	Audit Item	Control Description	Audit Method / Tool	Risk Level	Recommendation
1	Model Input Validation	Ensure inputs to the ML model are sanitized and validated	Review code, apply fuzz testing	High	Implement strict input validation and preprocessing
2	Adversarial Input Protection	Assess resistance to adversarial attacks (e.g., FGSM, DeepFool)	Test using adversarial ML frameworks	High	Train with adversarial samples, apply input noise or detection layers
3	Training Data Integrity	Ensure training data is accurate, relevant, and untainted	Review data lineage, perform statistical analysis	High	Validate sources and clean datasets regularly
4	Model Poisoning Defense	Prevent and detect poisoned or manipulated training data	Inspect for data anomalies	High	Use data provenance and anomaly detection
5	Access Control to Models	Restrict access to model files, APIs, and training data	Review IAM, API keys, and model storage ACLs	High	Implement RBAC, MFA, and API authentication
6	Model Explainability	Evaluate if model outputs can be explained and audited (e.g., SHAP, LIME)	Use explainability tools	Medium	Integrate explainability layers for regulated sectors
7	Bias and Fairness Analysis	Assess and mitigate model bias in data and decision-making	Run fairness audits (e.g., Equalized Odds, DemParity)	High	Retrain with balanced datasets and apply bias-mitigation algorithms
8	Model Version Control	Ensure models are versioned, traceable, and immutable after deployment	Review ML Ops platform (e.g., MLflow, DVC)	Medium	Use model registries and secure CI/CD pipelines
9	Secure Model Deployment	Ensure models are deployed securely (e.g., containerized, signed, isolated)	Review deployment pipeline and runtime environment	High	Use signed containers and isolate inference environments
10	API and Endpoint Security	Ensure model endpoints are protected from abuse (e.g., DDoS, scraping)	Review API Gateway and WAF configurations	High	Rate-limit, authenticate, and monitor all inference endpoints
11	Inference Data Privacy	Ensure user data sent for inference is protected (at rest and in transit)	Check for HTTPS, encryption settings	High	Use TLS, encrypt inference logs and outputs
12	Logging and Monitoring	Enable logging for access, model use, drift, and anomalies	Review logging pipeline and retention	Medium	Centralize logs, monitor usage and performance in real-time
13	Model Drift Detection	Monitor for statistical drift that may reduce model accuracy or fairness	Use monitoring tools (e.g., Alibi Detect, WhyLabs)	Medium	Set alerts and retrain models when drift is detected
14	Confidential Computing	Protect model execution with hardware-based isolation (e.g., Intel SGX, AWS Nitro)	Review hosting infrastructure	Medium	Use TEE for sensitive models and data
15	Data Minimization & Retention	Ensure minimal data is collected and retained for only necessary durations	Review data collection and retention policies	High	Apply anonymization and retention policies
16	Ethical Use & Consent	Confirm informed consent for training/inference using personal data	Review terms of service, consent forms	Medium	Ensure compliance with GDPR, HIPAA, PDPB
17	Model Reproducibility	Ensure ability to reproduce model results for auditing purposes	Check code, data, and environment tracking	Medium	Automate pipeline with reproducibility artifacts
18	Third-Party AI Service Audit	Ensure external AI services (e.g., SaaS APIs) meet security/compliance standards	Vendor assessment	High	Use certified, compliant providers and review SLAs
19	Intellectual Property Protection	Prevent theft or reverse engineering of proprietary models	Test for model extraction attacks	High	Use output obfuscation, watermarking, and rate limiting
20	Regulatory Compliance Mapping	Align model development and use with standards (e.g., ISO 23894, NIST AI RMF)	Map controls and documentation	High	Perform regular audits and gap assessments

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🛠️ Recommended Tools for AI/ML Security Audits

Tool	Use Case
IBM Adversarial Robustness Toolbox	Test and defend against adversarial attacks
Alibi Detect	Detect data drift, outliers, adversarial examples
MLflow / DVC	Model versioning and experiment tracking
OpenMined	Privacy-preserving AI frameworks (e.g., federated learning)
SHAP / LIME	Model explainability and interpretability
WhyLabs	Monitoring for ML models in production
Snyk / SonarQube	Secure code and dependency scans
OWASP AI Security Top 10	Framework for identifying common AI security risks
Nessus / Qualys	General system vulnerability scanners

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📄 Deliverables from an AI/ML Security Audit

Deliverable	Description
AI/ML Security Audit Report	Summary of vulnerabilities, misconfigurations, and risk exposure
Threat Model and Risk Assessment	Threat identification and prioritization based on AI risk scenarios
Remediation Plan	Recommended fixes, timelines, and ownership
Bias & Fairness Analysis	Assessment of ethical AI compliance (e.g., non-discrimination)
Compliance Mapping Report	Mapping against GDPR, ISO 27001, NIST AI RMF, etc.
Model and Pipeline Review	Evaluation of training, deployment, and inference workflows
Security Checklist	Baseline controls checklist for ongoing AI/ML development

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🔐 Compliance Frameworks & Standards

Standard	Relevance to AI/ML
ISO/IEC 23894:2023	AI risk management framework
NIST AI RMF	NIST AI Risk Management Framework
EU AI Act (Proposed)	Risk-based classification of AI systems (High-risk, etc.)
OECD AI Principles	Ethical guidelines for trustworthy AI
GDPR / PDPB	Data privacy in AI models
HIPAA	AI-based health diagnostics and data privacy

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Would you like help performing an AI product threat model, creating a secure ML pipeline, or aligning with upcoming regulations like the EU AI Act?