AI Risk Controls & Compliance
Controls that auditors and regulators can follow
AI systems introduce new risk categories that traditional control frameworks were not designed to address. Model drift, algorithmic bias, data quality degradation, and autonomous decision-making require purpose-built controls.
S.AI.L designs and implements AI-specific controls aligned to ISO 42001, NIST AI RMF, and EU AI Act requirements — with continuous automated testing and auditor-ready reporting. Every control is documented, tested, and traceable.
Compliance-first. Your cloud. No vendor lock-in. Principal-led.
60%
reduction in audit preparation time when AI automates control testing and evidence collection
Deloitte Risk Advisory, 2024
40%
reduction in control testing costs through continuous automated monitoring versus periodic manual testing
KPMG, 2024
3×
faster regulatory examination responses when control documentation and evidence are assembled automatically
PwC, 2024
How it works
Four stages. One governed workflow.
From control design to auditor-ready reporting — scroll through each stage to see how S.AI.L strengthens your AI risk controls
Stage 01
Control Design & Documentation
Define AI-specific controls aligned to ISO 42001, NIST AI Risk Management Framework, and EU AI Act requirements. Each control has documented objectives, owners, testing procedures, and evidence requirements. Control design is integrated into the AI system build — not retrofitted after deployment. Controls cover the full AI lifecycle: data quality, model training, deployment, monitoring, and decommissioning
What happens
- 1Map AI system risks to control objectives using ISO 42001 and NIST AI RMF frameworks
- 2Define control owners, testing frequencies, and evidence requirements for each control
- 3Integrate controls into the AI development lifecycle — design, build, test, deploy, monitor
- 4Document control procedures in auditor-accessible format with clear pass/fail criteria
Outputs
- AI-specific control framework
- Control ownership matrix
- Testing procedure documentation
- Evidence requirements specification
Stage 02
Automated Control Testing
Continuous automated testing of AI system controls replaces periodic manual testing. Model drift detection, bias monitoring, performance degradation alerts, and data quality checks run continuously. Evidence is collected and timestamped automatically — no manual evidence gathering at audit time. Anomalies trigger escalation workflows with full context
What happens
- 1Deploy automated testing pipelines for model performance, fairness, and robustness
- 2Monitor for model drift: statistical distribution shifts in inputs and outputs
- 3Run bias detection across protected characteristics with configurable thresholds
- 4Auto-collect and timestamp evidence for every control test — audit-ready by default
Outputs
- Continuous model drift detection
- Automated bias monitoring
- Performance degradation alerts
- Auto-collected control evidence
Stage 03
Regulatory Mapping & Gap Analysis
Map existing controls to regulatory requirements: EU AI Act, GDPR, sector-specific regulations (FCA, FDA, EMA), and internal policies. Gap analysis identifies where controls are insufficient, missing, or untested. Remediation is prioritised by regulatory risk and compliance deadline — August 2026 for EU AI Act high-risk systems
What happens
- 1Map each control to specific regulatory requirements (EU AI Act articles, GDPR, sector regulations)
- 2Identify gaps: missing controls, insufficient testing, or inadequate evidence
- 3Prioritise remediation by regulatory risk, compliance deadline, and business impact
- 4Track remediation progress against EU AI Act August 2026 compliance deadline
Outputs
- Regulatory-to-control mapping matrix
- Gap analysis report with risk ratings
- Remediation roadmap with priorities
- Compliance deadline tracking
Stage 04
Auditor-Ready Reporting
Generate control effectiveness reports, risk heat maps, and compliance dashboards that auditors and regulators can follow without technical translation. Reports are assembled from live control testing data — not manually compiled. Board-level summaries provide risk posture at a glance. Detailed drill-downs give auditors the evidence they need
What happens
- 1Auto-generate control effectiveness reports from continuous testing data
- 2Produce risk heat maps showing control status by AI system, regulation, and risk category
- 3Create board-level compliance summaries with trend analysis and exception reporting
- 4Assemble auditor-ready evidence packages with drill-down capability to individual test results
Outputs
- Control effectiveness reports
- Risk heat maps by system and regulation
- Board-level compliance summaries
- Auditor drill-down evidence packages
Who this is for
Built for the leaders who own the outcome
Chief Risk Officer
Continuous visibility into AI system risk posture with automated control testing, gap analysis, and board-ready reporting
Chief Compliance Officer
Regulatory-mapped controls with automated evidence collection — eliminating manual compliance processes and reducing deadline risk
Head of Internal Audit
Auditor-ready documentation assembled automatically from live data — no more quarter-end scrambles to collect evidence
Chief Financial Officer
Reduce compliance costs, accelerate audit cycles, and demonstrate governance maturity to investors and regulators
Ready to strengthen your AI risk controls?
Speak to a Principal Consultant about designing and implementing AI-specific controls that auditors and regulators can follow — with continuous automated testing