Responsible AI practices, threat modeling, privacy, bias mitigation, and regulatory compliance.
AI systems introduce unique security risks and ethical challenges that traditional security and compliance practices do not fully address. This discipline covers the specific threats, governance requirements, and ethical obligations that come with deploying AI in production — from adversarial attacks to algorithmic bias to emerging regulation.
Identify and assess threats specific to AI systems — adversarial inputs, data poisoning, model extraction, membership inference, and prompt injection. Conduct threat modeling as part of the development lifecycle, not as an afterthought before launch.
Evaluate models for bias across protected attributes before and after deployment. Define fairness metrics appropriate to the use case (demographic parity, equalized odds, calibration). Build bias testing into the CI/CD pipeline so it runs automatically with every model update.
Provide appropriate explanations for model decisions. High-stakes applications (lending, hiring, healthcare) require interpretable models or robust post-hoc explanation methods. Document model capabilities, limitations, and intended use cases in model cards.
Implement privacy controls at every stage — differential privacy in training, data minimization in feature engineering, secure inference for sensitive inputs, and right-to-erasure compliance. Conduct privacy impact assessments for AI systems that process personal data.
Stay current with AI-specific regulation (EU AI Act, NIST AI RMF, industry-specific guidelines) and implement compliance controls proactively. Maintain audit trails, documentation, and governance structures that demonstrate compliance.
Harden the ML infrastructure stack — secure model artifact storage, encrypt data in transit and at rest, implement access controls on training data and model endpoints, and audit access patterns. Treat ML infrastructure with the same security rigor as any production system.