AI pillar

Evaluate AI with evidence, not momentum

Use this hub to connect an AI use case, model or vendor claim to evaluation evidence, governance ownership, procurement requirements, and current primary-source guidance.

AI policy and implementation guidance change quickly. Dated Zeph Tech research preserves publication context; current legal, regulatory, and government-policy decisions should be checked against the primary source before action.

Define the evaluation Use the procurement toolkit How evidence is reviewed

Start with the use case

Separate capability from suitability

A model can be impressive and still be the wrong system for the workflow. Define the decision, affected users, consequences, data, dependencies, and acceptable failure modes before comparing products.

1. Define the use case

Document the task, decision authority, users, affected parties, data sources, human review, downstream actions, and what happens when the AI is wrong or unavailable.

2. Classify the risk

Identify legal, rights, safety, security, privacy, records, accessibility, procurement, and mission consequences. Do not infer a legal classification from a vendor label.

3. Define evaluation evidence

Specify representative test data, quality thresholds, prohibited outcomes, robustness checks, human-review criteria, logging, change controls, and acceptance evidence.

4. Assign lifecycle ownership

Make owners explicit for approval, data, model or service changes, monitoring, incidents, complaints, records, vendor escalation, re-evaluation, and retirement.

Evaluation evidence

Test the system you will actually operate

Benchmarks and demonstrations can inform discovery, but acceptance should use the organization’s workflow, data boundaries, users, failure conditions, and decision thresholds.

Evaluation brief

Capture the operating problem, stakeholders, constraints, evidence requirements, dependencies, unknowns, and buyer questions before product comparisons begin.

Build an evaluation brief

Security evidence

Evaluate identity, data protection, secure development, vulnerability handling, logging, incident response, resilience, third parties, and assurance evidence around the AI service.

Use the vendor security questionnaire

Accessibility evidence

AI-enabled interfaces do not remove accessibility obligations. Require testable evidence for the actual user experience, including generated or dynamic states.

Review accessibility evidence

Scoring and acceptance

Weight evidence before demonstrations, record exceptions, and keep acceptance criteria distinct from sales claims or generalized model benchmarks.

Use the evaluation scorecard

Acquisition and lifecycle

Carry AI requirements beyond the initial purchase

Before award

Define permitted and prohibited uses.
Identify data-use, retention, training, residency, and subprocessor boundaries.
Require meaningful model/service change notification.
Define evaluation, audit, incident, and records evidence.
Specify integration, identity, portability, and accessibility requirements.

After award

Re-evaluate material model, policy, data, or workflow changes.
Monitor quality, safety, security, complaints, incidents, and exceptions.
Preserve decision records and evidence needed for oversight.
Exercise continuity and fallback processes.
Keep export, migration, and exit rights operational rather than theoretical.

Follow the procurement lifecycle Review continuity and exit

Dated research

Published AI briefings

Briefings capture vendor, policy, model, and implementation context available at publication. Re-check current product behavior and authoritative policy sources before relying on an older article.

View AI briefings Browse all research

Verify at the source

Primary AI governance sources

Use these pages to confirm the current version, implementation status, or government guidance before turning a Zeph Tech summary into an operational or compliance decision.

Risk management and evaluation

NIST AI Risk Management Framework — current framework resources, revision status, profiles, playbook, and supporting material.
NIST AI Resource Center — operational resources for AI risk management, testing, evaluation, verification, and validation.
NIST Generative AI Profile — cross-sector generative-AI risk considerations and suggested actions.

Government and regulatory status

European Commission AI Act page — current application timeline, implementation material, and links to official EU resources.
EU AI Act — EUR-Lex — official regulation text and consolidated legal record.
OMB memoranda — authoritative federal memoranda, including current AI use and acquisition guidance.

Zeph Tech does not treat a superseded memorandum, an old implementation date, or an earlier model release as current merely because an older article remains online. See the editorial standards for source hierarchy and historical-content handling.

Next step

Make the AI decision reviewable

Move from use case to evidence, scoring, implementation, monitoring, and exit with one connected decision trail.

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