Give governance and risk a system-level record
Keep an AI inventory with owners, intended use, models, components, agents and suppliers. Connect risk and impact assessments to policies, lifecycle gates, controls and exceptions. Record who may accept residual risk and who can restrict a use when conditions fail.
AI-BOM and ML-BOM records preserve component dependencies, versions, hashes and provenance. Third-party evidence remains linked to the supplier and configuration it covers. Framework mapping connects applicable NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, EU AI Act and internal-control requirements to the work and evidence; a mapping is not a compliance determination.
Turn evaluation into an assurance decision
Connect requirements and acceptance criteria to scenarios, metrics, results and findings. Review whether evidence is relevant, sufficient and current before deciding. Retain rejected and inconclusive findings alongside successful tests so the record explains the limits as well as the conclusion.
See Assurio in Practice Preserve the evidence behind the decision
Trace each result to the evaluated system, domain, requirement baseline and method. Preserve provenance, review history and controlled evidence revisions. The evidence ledger connects published records to later amendments rather than silently overwriting the basis of an approval.
A reviewer should be able to follow a requirement to its evidence, identify an unresolved finding and understand why the decision covers one operating condition but excludes another. Signed artifacts and protected records support integrity controls; retention and immutability policies must fit the deployment architecture.
Govern adaptive, agentic and autonomous behavior
For adaptive systems, define what may change and which evidence conditions permit continued use. For agents, connect identity, tool permissions, action boundaries, escalation and human oversight to the system record. For autonomous systems, evaluate operating limits, fallback and intervention across the assembled system.
Runtime controls enforce configured boundaries at the relevant execution points. Operational events become evidence for review; they do not turn monitoring into a universal guarantee of safe behavior.
See Assurio in Practice Keep change connected to evidence
A model replacement, permission change, revised threshold or new operating environment can affect different parts of the assurance record. Trace those dependencies, identify stale evidence and direct revalidation toward the claims that need it. Preserve the reason for reusing unaffected evidence.
See Assurio in Practice Connect the tools your teams already use
Bring evidence from requirements systems, model registries, MLOps, observability, simulation and test infrastructure into the assurance process. Use API and integration contracts to preserve identifiers, versions and provenance across tools.
Connect enterprise GRC, ERP, CRM and workflow systems where AI decisions affect business operations. Define integration ownership, event semantics, access controls and failure handling for each implementation; a connector list alone does not establish reliable evidence exchange.
Deploy for the operating environment
Customer-hosted and dedicated deployment designs support controlled enterprise use. Connected edge and disconnected operation require explicit synchronization, data-freshness and authorization rules. Separate execution workers, protect evidence access and define role- and attribute-based permissions.
Deployment planning establishes security controls, retention, recovery, integration scope and the evidence that can be shared with reviewers. Choose these against the actual environment rather than assuming the same architecture fits every system.
Start with the decision in front of you
Use the AI System Inventory Template to establish scope and ownership. Use the AI Assurance Checklist to identify missing evidence before a review. For organizational responsibilities, the Enterprise AI Governance Operating Model provides the wider context.
