ISO/IEC 42005 impact assessment tools compared: templates, GRC modules, and runtime telemetry

What the standard asks for, and what only exists after deployment

ISO/IEC 42005:2025 provides guidance for organizations conducting AI system impact assessments. The assessments focus on how an AI system and its foreseeable applications may affect individuals, groups or society at large. ISO describes the aim as helping organizations identify, evaluate and document potential impacts across the AI system lifecycle, in support of transparency, accountability and trust.

The standard recommends performing assessments from design and development through deployment and post-market monitoring, and updating them as needed. It is positioned as complementary to ISO/IEC 42001 on AI management systems, along with ISO/IEC 38507 on AI governance and ISO/IEC 23894 on AI risk management. An assessment record therefore belongs inside the wider management system rather than standing alone.

It helps to split the evidence into two halves. The design-time half covers expected use, anticipated impacts and the decisions reviewers made about them. The post-deployment half covers monitoring results, incidents and feedback. The NIST AI RMF points the same way. Its Map 5.1 asks for the likelihood and magnitude of each identified impact, beneficial and harmful, to be documented, drawing on expected use, past uses in similar contexts, incident reports and feedback from people outside the team. Some of those inputs exist only once a system is running. Any tool comparison should ask which half each category serves.

Comparing the three categories on the same criteria

The table summarizes typical characteristics of each category. Individual products vary, so treat it as a framework for evaluation rather than a verdict.

• Lifecycle coverage: Templates: design and pre-deployment, with post-deployment only if someone revisits them. GRC modules: the same, plus scheduled review cycles. Runtime telemetry: production behavior only, with no view of design intent.
• Evidence durability: Templates: only as durable as the document store and version discipline. GRC modules: versioned records with retained history. Runtime telemetry: durable if retention is configured and records are tamper-resistant.
• Update cadence: Templates: manual and event-driven. GRC modules: workflow-driven reminders and triggers. Runtime telemetry: continuous, generated as the system operates.
• Reviewer workflow: Templates: informal unless wrapped in a process. GRC modules: assignment, approval and sign-off. Runtime telemetry: no reviewer workflow, though it can route items for human approval.
• Auditor usability: Templates: readable but hard to reconcile with actual behavior. GRC modules: strong on who decided what and when. Runtime telemetry: strong on what happened, weak on why a decision was made.
• Operational effort: Templates: low to start, rising as systems multiply. GRC modules: setup and process ownership. Runtime telemetry: instrumentation and a review path for the volume of records.

Mapping documentation, workflow and monitoring to each category

Documentation is the template's strength. A structured template forces consistent fields for system purpose, affected parties, anticipated impacts and supporting evidence. NIST's draft documentation guidance says artifacts should be easy to create, extend, update and maintain, which is a useful test for any template. The weakness is drift. A template completed at launch describes intended behavior and ages as the system, its users and its context change.

Assessment workflow is the GRC module's strength. Versioning, assignment, independent review and sign-off let an auditor reconstruct who decided what and when. NIST notes that independent review can mitigate internal bias and conflicts of interest, which makes reviewer separation a real selection criterion. A GRC module still holds what people entered. It does not observe the system.

Monitoring is where runtime telemetry applies. It records actual behavior, which is the evidence needed to test whether anticipated impacts match production reality and to revisit likelihood and magnitude estimates. NIST Manage 4.1 asks for post-deployment monitoring procedures covering negative and positive impacts, and for re-calibrating risk tolerances using monitoring information. Telemetry is the raw material for that loop, but someone still has to interpret it and decide what changes in the assessment.

When a combination is needed, and what telemetry cannot do

A single category rarely satisfies the whole lifecycle. A template with no workflow gives weak reviewer evidence. A GRC module with no production data leaves the post-market half dependent on manual updates. Telemetry without an assessment record has no stated intent to compare against. For a regulated enterprise with AI systems in production, the practical pattern is a structured assessment record, a workflow that versions and reviews it, and a defined path that carries runtime evidence into scheduled and event-driven updates.

Telemetry also has limits. The NIST Playbook observes that many AI outcomes and impacts may not be visible to the people building and operating a system, and that impact assessment is a two-way effort that can require direct feedback from end users and impacted groups. System-generated records cover part of the evidence. Stakeholder engagement, contextual analysis and feedback channels remain separate inputs. NIST's Measure guidance also asks whether measurements generalize to other contexts, a reminder that one evidence type should not be stretched beyond what it shows.

Runtime policy enforcement as a source of assessment evidence

Trussed is runtime control, not a GRC platform, an assessment template or a tool that performs the assessment. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants. Policy decisions are allow, redact, block, rate limit or escalate for human approval, and they are enforced at runtime, not reviewed after the fact. Each enforcement action generates audit-ready evidence of what the AI was allowed to do and what it did. That evidence is mapped to HIPAA, NAIC, NYDFS, EU AI Act and NIST requirements.

For an impact assessment, this gives the post-deployment half a continuous record that reviewers can draw on when they revisit anticipated impacts. Trussed has run in continuous production in a regulated enterprise for more than 18 months and generates more than 30 GB of audit evidence per day. It can be self-managed in the customer cloud, with configurable retention and data residency.

The scope is bounded. Evidence covers individual interactions and tool calls. It does not provide transaction-level governance or end-to-end tracing of agentic workflows. It supports an impact assessment process and does not replace it.