AI GRC in 2025: The Evidence-First Playbook

AI has turned compliance from an annual event into a continuous activity. When a model is updated weekly, a quarterly control review is already out of date. The result is not just regulatory risk; it is trust risk. ## Why AI breaks traditional GRC Most GRC programs were built for stable systems. AI systems are not stable: - behavior shifts with new data, prompts, and fine-tuning - third-party models change without notice - outputs are probabilistic, not deterministic - business teams can deploy AI features without realizing they changed the risk profile If you cannot show evidence of what your AI did, who approved it, and how you are monitoring it, you do not have AI GRC. You have paperwork. ## The evidence-first loop Think of AI GRC as a loop you can run every month, not a binder you refresh once a year. 1) Inventory List every AI system, model, and workflow. Include prompts, data sources, vendors, and where outputs are used. 2) Classify risk Decide which systems are high impact. Use criteria like safety, discrimination risk, regulatory scope, and business criticality. 3) Build controls Add guardrails that show intent and discipline: access controls, human review, model risk assessments, logging, and red-team testing. 4) Monitor Track drift, incidents, and usage. The best evidence is time-stamped evidence. 5) Report Package the results into artifacts an auditor can read in minutes. ## The minimum evidence pack If you want to be audit-ready, these artifacts do most of the work: - AI system inventory with owners and risk tier - model cards and data lineage summaries - human oversight and approval records - incident log and response playbooks - training completion and policy attestations ## What AI literacy should mean Most organizations treat AI literacy as a one-hour video. That is not enough. Build a tiered program: - Builders: secure prompt design, data handling, evaluation, and risk mapping - Deployers: when AI is appropriate, how to interpret outputs, and when to escalate - Leaders: governance decisions, risk appetite, and regulatory implications Make it practical. Use real workflows, not generic examples. ## A 30-day sprint that works Week 1: Inventory everything in production and in pilot. Week 2: Run a risk workshop and remove or redesign any prohibited or high-risk uses. Week 3: Implement logging, approvals, and human review for critical workflows. Week 4: Assemble your evidence pack and run a tabletop audit. The goal is not perfection. The goal is proof. ## The payoff Organizations that treat AI GRC as a continuous evidence loop move faster, not slower. They can ship AI features with confidence because they can show how the system behaves, who owns it, and how it is controlled. If you want an AI GRC checklist or a template evidence pack, reach out. We are building them now.