Controls & Guardrails

From Risk to Protection

Risk assessment identifies what could go wrong with AI systems. Controls and guardrails are the protections you implement to prevent those risks from materializing. Effective controls are the difference between AI governance as aspiration and AI governance as reality.

Controls exist on a spectrum from preventive to detective to corrective. Preventive controls stop problems before they occur — for example, blocking sensitive data from being sent to public AI services. Detective controls identify issues when they happen. Corrective controls respond to problems after detection. A comprehensive control framework includes all three types, creating defense in depth.

The challenge with AI controls is balancing protection with enablement. The goal is intelligent controls that are risk-proportionate — stringent for high-risk AI applications, lighter-touch for low-risk uses.

Technical Controls

1. Data Protection Controls

Protecting sensitive data is fundamental to AI governance. Implement data classification and handling requirements, deploy data loss prevention (DLP) tools, use encryption for data at rest and in transit, and implement access controls. For particularly sensitive use cases, consider privacy-enhancing technologies like differential privacy, federated learning, or synthetic data generation.

2. Model Security Controls

AI models are valuable assets requiring protection. Implement access controls limiting who can access, modify, or deploy models. Use model versioning and change management. Protect models from extraction attacks through model watermarking, API rate limiting, and output filtering. Implement input validation to detect adversarial inputs and deploy anomaly detection for unusual patterns.

3. Explainability and Transparency Controls

For AI applications affecting individual rights or high-stakes decisions, implement controls providing appropriate transparency. This might include feature importance scores, counterfactual explanations, confidence scores, or audit trails. The level of explainability should match the risk and regulatory requirements of the use case.

Process Controls

1. Approval Workflows

Implement approval workflows matched to AI system risk levels. High-risk AI systems should require review from multiple stakeholders. Approval workflows should be built into development processes, not bolted on afterward. Make approval processes clear and efficient — lengthy, unclear processes encourage teams to work around governance.

2. Testing and Validation Requirements

Establish mandatory testing before AI systems can be deployed. Testing should cover functional performance and governance requirements including fairness across demographic groups, robustness to adversarial examples, privacy protections, and regulatory compliance. Testing should not be a one-time gate but an ongoing practice.

3. Human Oversight and Review

For AI systems making or influencing important decisions, define what decisions require human review, who is qualified to perform that review, and how decisions are documented. Be aware of automation bias — the tendency for humans to over-rely on AI recommendations. Combat it through reviewer training and presenting AI outputs in ways that encourage critical thinking.

Incident Response

Despite preventive controls, AI incidents will occur. Establish specific incident response procedures for AI systems. Define what constitutes an AI incident and create clear escalation paths. Build relationships between AI governance teams, incident response teams, legal counsel, and communications teams before incidents occur. Conduct tabletop exercises to practice AI incident response.

Making Controls Practical

Design controls to be as frictionless as possible while still providing necessary protection. Automate control enforcement where feasible. Integrate controls into existing tools and workflows. Regularly review control effectiveness — the best controls strike the right balance between protection and enablement, evolving as your organization's AI capabilities and risk landscape mature.