1. Assemble your cross-functional response team
Compliance readiness starts with clear ownership. Establish an accountable group that spans legal, security, product, and the AI/ML engineers responsible for model delivery. Document who signs off on AI deployments, who maintains risk registers, and who provides user communications.
For most teams this means appointing a Responsible AI lead who pairs with legal counsel. Tie these roles to concrete expectations: cadence of reviews, escalation paths for model incidents, and the service-level agreement for responding to new regulatory obligations.
2. Build an AI system inventory with context
Every AI governance framework begins with a full inventory of systems, models, and data pipelines. Start by listing every AI-enabled touchpoint, including public-facing experiences that FRAI can scan automatically (marketing sites, product flows, support bots).
For each entry capture the business purpose, user impact, training data categories, and whether personal data is processed. FRAI’s website scan results map detected AI elements to the inventory so you can cross-check shadow deployments.
Add metadata required by the EU AI Act: the system owner, the risk category (minimal, limited, high, prohibited), and the lifecycle stage (design, development, testing, production).
3. Classify risk and assign safeguards
Once the inventory exists, classify each system. High-risk AI (for example, systems affecting employment, credit, or access to critical services) must adhere to Articles 9-15 of the EU AI Act. Limited-risk chatbots require disclosure and frictionless opt-out.
Use FRAI's compliance scoring to prioritize remediation: the platform highlights absent disclosures, missing transparency language, and potential bias vectors exposed in chatbot testing. Align the findings with your internal control framework, including ISO/IEC 42001, and document which safeguards must be implemented before launch.
4. Operationalize data quality and documentation
Regulations expect you to prove that training and evaluation data meet quality thresholds. Capture provenance, consent, and known limitations. Establish a repeatable process for updating documentation whenever models retrain or prompts change.
FRAI’s manual test surveys and chatbot test histories give you human-in-the-loop evidence that your team reviewed AI behavior. Link these reports directly in your documentation package so auditors can trace issues to remediation actions.
5. Stand up monitoring and incident response
Readiness is not a one-time activity. Define the metrics you will watch in production: model drift, latency, user complaints, and policy violations. FRAI can trigger alerts when scans detect new AI embeds or when chatbot responses fall outside risk tolerances.
Codify the response workflow. Who triages incidents? How quickly do you suspend or roll back a problematic model? Where are incident reports stored? Tie these answers to your broader security and privacy programs so AI is not an island.
6. Communicate transparently to stakeholders
Transparency is a regulatory mandate and a trust signal. Publish AI system summaries that state purpose, limitations, and human oversight paths. Align customer communications with the disclosures FRAI checks for during scans.
Internally, brief executives on risk posture using FRAI dashboards. Share progress toward compliance milestones and highlight upcoming regulatory deadlines so leadership remains invested in the program.
7. Schedule continuous improvement
Set quarterly or release-based reviews to reassess controls. Use FRAI’s historical trend lines to show improvements in compliance scores, test coverage, and risk reductions. Pair these insights with lessons learned from incidents or user feedback to refine policies.
As the EU AI Act moves into enforcement, expect delegated acts to clarify requirements. Continually compare your backlog against the latest guidance and adapt checklists accordingly.
Next steps
Use FRAI Scan to baseline your public surfaces and FRAI Test to evaluate conversational AI. Once you have the initial reports, create a remediation backlog and loop in stakeholders via the dashboard. Need help configuring your program? Get started with free coins to begin scanning and testing your AI systems.