AI Security Hub
08AI Security Pillar

Feedback and Continuous Improvement

Keep AI controls current as models, data, tools, business processes, and risks change.

Business focus

Test and improve controls

Why it matters

Security starts with a defined business boundary

Continuous improvement turns human review, incidents, evaluation results, and business changes into updated policies, tests, and guardrails.

Business risk

An AI control can pass its launch review and become ineffective later. Models change, data shifts, integrations expand, and employees discover new uses.

What this pillar covers
  • Human-review feedback
  • Policy and workflow updates
  • Evaluation and adversarial testing
  • Guardrail and threshold tuning
Launch reading list

Start with these practical guides

Each pillar begins with one anchor guide and two supporting articles. Published guides become active automatically as they enter the blog.

Publishing soon

AI Red Teaming: How to Test a Business AI Workflow Before It Fails

Test identity, data, retrieval, model behaviour, tools, approvals, and response.

Part of the launch series
Publishing soon

AI Model Governance: A Practical Lifecycle for Business

Assign decisions and evidence across design, approval, operation, and retirement.

Part of the launch series
Publishing soon

AI Robustness Testing: Will the System Behave Safely When Conditions Change?

Test failure behaviour across realistic, unusual, and adversarial conditions.

Part of the launch series

Questions for leadership

  • Which events require a policy, test, or workflow update?
  • How often are important AI use cases reevaluated?
  • Who accepts residual risk after a failed test?
  • Can the team prove that a fix remains effective after later changes?
Practical control checklist

Put the pillar into practice

  1. 1Maintain an evaluation set for each important use case.
  2. 2Record reviewer findings, incidents, exceptions, and user feedback.
  3. 3Assign owners and deadlines to failed controls.
  4. 4Add fixed failures to regression testing.
  5. 5Repeat approval when the model, data, tools, or purpose changes.
Review a real workflow

Turn this pillar into operating controls

Map the data, access, approvals, monitoring, and evidence around one important AI use case before expanding it.

Explore the diagnostic