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Types of AI Controls: Governance, Technical, Legal, and Ethical Safeguards
Safely building, operating, and retiring intelligent technologies takes a broad ecosystem of safeguards. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series maps out the major categories of AI controls that auditors are expected to validate — spanning executive oversight, technical security, day-to-day operations, development processes, legal compliance, and ethical considerations — and frames the auditor’s role across that entire control environment.
What this episode covers
- The auditor’s golden rule — validate controls and gather evidence, but never execute the controls yourself.
- Governance and organizational controls — accountability with RACI charts, executive ownership, policies, registries, and risk committees.
- Technical, security, and privacy controls — data quality, access management, configuration, testing, privacy techniques, and incident response.
- Operation and life cycle management — documentation, explainability, traceability, change management, containerization, and drift monitoring.
- System development life cycle (SDLC) controls — methodology-dependent controls across ideation, design, build, and deployment.
- Legal, risk, compliance, and regulatory controls — iterative risk assessments, FRIA and DPIA, contract management, whistleblower channels, and regulatory reporting.
- Ethical and human values controls — consent management, bias testing, transparency tooling, and mandatory human oversight with appeal rights.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the six categories of AI controls?
The six categories are governance and organizational controls, technical security and privacy controls, operation and life cycle management, system development life cycle (SDLC) controls, legal risk compliance and regulatory controls, and ethical and human values controls. Together they form an interconnected web that protects an AI system from the boardroom to the technical firewalls to human ethics.
What is the role of an auditor in the AI control environment?
The golden rule is that an auditor validates controls but does not execute them. Like a theme park safety inspector who reviews maintenance logs rather than tightening bolts personally, the auditor gathers evidence proving the engineering team ran the tools, correctly interpreted the results, and met company requirements. Many existing cybersecurity and privacy protocols simply need to be expanded to cover AI.
What is model drift and why does it matter?
Model drift happens when a system loses its accuracy over time because the real-world environment it operates in has changed. A system trained to predict fashion trends on data from the 1990s would produce entirely wrong predictions today because society drifted away from those styles. Continuous monitoring must stay active after deployment specifically to detect this drift.
What are LIME and SHAP in AI explainability?
LIME stands for Local Interpretable Model-Agnostic Explanations and isolates why one single specific prediction was made, like a teacher explaining why you lost points on one essay question. SHAP stands for SHapley Additive exPlanations and provides a broader view, showing the overall importance and weight of different features across all predictions, like reading the syllabus rubric for the entire semester.
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Reference: This article is based on concepts discussed in Types of AI Controls: Governance, Technical, Legal & Ethical.