A structured approach for managing AI risks across the lifecycle, from design through deployment. Align with the EU AI Act and ISO 42001 simultaneously using the four core functions.
The NIST AI Risk Management Framework (AI RMF 1.0) helps organisations identify, assess, and manage AI-specific risks, including bias, explainability failures, adversarial attacks, and unintended consequences. Scyverge maps your AI systems to the four core functions and builds a governance and operational risk programme aligned with both the EU AI Act and ISO 42001.
Catalogue all AI systems per GOVERN 1.1. Document deployment contexts, intended uses, and affected stakeholders to establish risk context for each system.
Categorise AI systems by risk type and context per MAP 1.1 through MAP 1.6. Map foreseeable harms and stakeholder impacts to organisational risk tolerance.
Evaluate AI systems against the seven trustworthiness characteristics per MAP 2.1 through MAP 2.3. Assess accuracy, fairness, robustness, privacy, security, explainability, and safety.
Assess current practices against all AI RMF subcategories across the four core functions. Prioritise gaps in GOVERN, MAP, MEASURE, and MANAGE with a remediation plan.
Conduct technical bias evaluations per MEASURE 2.6 and MEASURE 2.7. Measure disparate impact and fairness metrics across protected groups in AI decision-making systems.
Test AI systems for adversarial vulnerabilities per MEASURE 2.8. Identify evasion attacks, data poisoning, and model extraction risks that compromise trustworthiness.
Establish GOVERN 1 policies and accountability structures for AI risk governance. Define roles, risk tolerances, and oversight mechanisms across the AI lifecycle.
Define AI risk policies, oversight roles, and risk tolerance statements per GOVERN 1.2 through GOVERN 1.7. Build accountability structures across organisational levels.
Prioritise risks from MAP and MEASURE functions per MANAGE 1.1. Implement controls, document risk treatment decisions, and establish escalation paths per MANAGE 2.
Map AI RMF outcomes to EU AI Act Article 9 conformity requirements. Align risk classification, technical documentation, and CE marking support for high-risk systems.
Use AI RMF as the risk management foundation for ISO 42001 Clause 6. Align GOVERN, MAP, MEASURE, and MANAGE outputs with the AIMS continuous improvement cycle.
Build an AI risk register per GOVERN 1.3. Document identified risks, severity ratings, treatment plans per MANAGE 1, and residual risk acceptance for all in-scope systems.
Implement MANAGE 2.1 monitoring dashboards and incident response processes. Maintain ongoing review cycles across deployed AI systems per GOVERN 6.
Establish AI-specific incident procedures per MANAGE 2.2. Handle model failures, bias incidents, adversarial attacks, and unintended outputs in production systems.
Track performance degradation, data drift, and concept drift per MEASURE 2.9. Deploy automated alerting and retraining triggers for production AI systems.
Conduct scheduled re-assessments per GOVERN 6.2. Update AI risk profiles as systems evolve, new use cases emerge, or regulatory requirements change.
Deliver role-based training per GOVERN 2.1 through GOVERN 2.4. Cover data science, product, engineering, and governance teams with annual refreshers and scenario exercises.
Monitor evolving AI regulations and NIST AI RMF companion resources per GOVERN 6.1. Track standards updates to keep your governance programme current and aligned.
Teams building foundation models, ML pipelines, or AI-powered products need a structured approach to managing model risk, bias, and adversarial robustness throughout the development lifecycle.
Organisations integrating generative AI, LLMs, or decision-making AI into business processes must govern their deployment context, monitor for drift, and manage vendor AI risk.
The AI RMF is directly referenced in EU AI Act conformity pathways. Organisations targeting EU market access can use AI RMF alignment as a foundation for EU AI Act high-risk classification compliance.
A structured six-phase process from AI system inventory and context mapping through to continuous improvement and standards alignment.
Catalogue all AI systems in scope, classify by risk profile, and map against the AI RMF trustworthiness characteristics and stakeholder impacts.
Define AI risk policies, oversight roles, accountability structures, and risk tolerance statements aligned to the GOVERN core function.
Conduct technical and process evaluations for bias, robustness, explainability, security, and privacy across priority AI systems using the MEASURE function.
Prioritise and implement risk treatment plans, operational controls, and escalation paths for residual risks using the MANAGE function.
Deploy monitoring dashboards, AI incident response procedures, and drift detection with automated alerting for deployed AI systems.
Periodic re-assessment of AI risk profiles, tracking regulatory evolution, and aligning with EU AI Act and ISO 42001 as requirements evolve.