The short answer
Use AI to support analysis and keep decisions with the responsible people.
A controlled evaluation showing query classes, prohibited queries, ground-truth clips, retrieval precision and recall, missed critical clips, summary factuality, citation behavior, operator correction, evidence preservation, and approved-use boundaries.
Fit before tools
Use this workflow only when the operating conditions fit.
Use it when
- The platform and data processing are approved for the video environment.
- The intended query classes can be defined and tested.
- Original media, permissions, audit logs, and chain-of-custody controls remain intact.
- Trained reviewers verify every consequential result.
Do not use it
- To identify a person, infer intent, or create allegations from generated descriptions.
- To replace exhaustive review where policy or investigation requires it.
- To use prompts that encode protected or speculative characteristics without specific lawful approval.
Prepare first
Define the approved input before opening an AI tool.
Inputs to prepare
- Approved query taxonomy
- Representative positive, negative, and ambiguous clips
- Camera and scene condition matrix
- Ground-truth event intervals
- Expected citation to camera and time range
- User role and permission model
- Export, audit, and evidence-preservation procedure
- Model and index version
Keep out of the workflow
- Person identification or protected-trait inference outside approved use
- Generated text entered as evidence without source verification
- Queries that exceed the user’s video permissions
- Indexing or retention beyond approved boundaries
- Unlogged model or prompt changes
Default rule: If the information boundary is not explicit, do not paste, upload, connect, or transmit the material. Practice with made-up information until the responsible owner approves the tool and information you can use.
Implementation workflow
Complete the work in six reviewable steps.
- 01
Define query classes
Start with observable, operationally relevant descriptions such as vehicle enters loading area during a time window. Separate object, action, direction, color, count, and temporal queries. Ban intent and identity inference.
- 02
Build ground-truth sets
For each query class create known relevant, irrelevant, and ambiguous clips across representative scenes. Record exact intervals and adjudication notes.
- 03
Test retrieval
Measure precision at useful cutoffs, recall, missed critical clips, ranking position, duplicate results, and search latency. Test synonyms, negation, counts, direction, and phrasing changes.
- 04
Test summaries separately
Compare each generated statement to the cited clip interval. Classify supported, unsupported, contradicted, omitted material fact, and uncertain. Do not let a good retrieval score stand in for summary factuality.
- 05
Exercise permissions and evidence
Confirm results never exceed the operator’s camera, time, or case permissions. Verify audit logs, original-media link, export controls, hash or evidence process, and retention behavior.
- 06
Approve bounded use
State which query classes and conditions are allowed, required human verification, prohibited decisions, monitoring, user training, version traceability, and rollback or disable procedure.
Copyable working aid
Use this template, then adapt it to the approved workflow.
The template deliberately exposes missing evidence and preserves human approval. Replace bracketed fields; do not paste prohibited information.
VIDEO LANGUAGE EVALUATION
Intended use: [analyst task]
Approved query classes: object | action | direction | count | time
Prohibited queries: identity | intent | protected traits | allegation
GROUND TRUTH
query_id | query | relevant_clip_ids | ambiguous_clip_ids | condition tags
RETRIEVAL METRICS
precision@5 | precision@20 | recall | missed critical clips | median rank | latency
SUMMARY REVIEW
statement | cited clip/time | supported | unsupported | contradicted | omission | uncertainty
CONTROL TESTS
role permissions | audit log | original-media link | export | retention | version
APPROVAL
allowed conditions | human verification | monitoring | rollbackWorked example
A finished example you can check.
These fictional examples and corrections illustrate the review process. They are not records of real incidents or measured model performance.
The query red delivery vehicle enters loading area retrieves the correct clip plus two visually similar orange service vehicles. The summary states the vehicle delivered a package, which the video does not establish.
Ground-truth query set, ranked results, generated summary, and cited intervals.
VIDEO SEARCH EVALUATION — RETRIEVAL AND SUMMARY Query: “red delivery vehicle enters loading area.” Reviewed results: Three clips at the selected cutoff. One is relevant; two show visually similar orange service vehicles. Precision for this reviewed set is 1/3 (approximately 33%). Recall cannot be established from these three results. Unsupported summary: The vehicle delivered a package. The supplied evidence does not establish delivery. Corrected observation: “A red vehicle enters the loading area.” Preserve original clip and cited interval for direct verification. Analyst action: Review original footage before using the observation. Exclude orange-vehicle clips from the relevant set; preserve them in the evaluation record as retrieval errors. Disposition: Record retrieval relevance and summary support separately. A relevant clip does not validate every generated statement.
- Replace package delivery with observable movement. Query wording is not evidence of delivery.
Quality control
Review the artifact and measure whether it improved the work.
Release checklist
- Queries describe observable attributes and actions within approved purpose.
- Ground truth covers representative scenes, phrasing, and ambiguous cases.
- Retrieval and summary factuality are measured separately.
- Every result links to original media and a specific time interval.
- Role permissions, audit, export, retention, and evidence controls are tested.
- Approved use requires trained human verification and version traceability.
Measures worth tracking
- Precision at the result cutoff operators use
- Recall and missed critical clips
- Median rank of first relevant clip
- Unsupported or contradicted summary statements
- Operator correction and time-to-verified-clip
- Permission or audit control failures
- Performance by scene and query class
Stop conditions
Treat these outcomes as failures, not minor editing issues.
A generated description is copied into a report without video verification.
Query wording implies intent or identity.
The search index returns cameras outside user permission.
Model or index updates change performance without regression testing.
Escalate instead of improvising: Site-specific risk assessment, emergency action, legal interpretation, employment action, identity determination, biometric use, and consequential access or dispatch decisions require the approved professional and organizational process.
Practical questions
Questions to resolve before operational use.
Is a video summary evidence?
No. Treat it as a navigation aid. The original video, associated metadata, and approved evidence process remain authoritative.
How should hallucinations be measured?
Review each consequential summary statement against the cited interval and classify unsupported or contradicted claims separately from omissions and uncertainty.
Can natural-language search replace manual review?
It may reduce the candidate set for approved tasks. Exhaustive or legally required review still follows the applicable procedure, and every selected clip requires trained verification.
Sources and scope
Use authoritative guidance, then apply the organization’s own requirements.
- NIST AI Risk Management Framework Voluntary framework for governing, mapping, measuring, and managing AI risk across the lifecycle.
- NIST AI Resource Center Operational resources for testing, evaluation, verification, and validation of AI systems.
- CISA Guidelines for Secure AI System Development Secure-by-design guidance covering development, deployment, and operation of AI systems.
This guide is vendor-neutral practitioner planning guidance, updated 2026-09-07. It is not a compliance determination, site risk assessment, emergency procedure, or substitute for qualified legal, privacy, cybersecurity, safety, engineering, or security review. Product capabilities and applicable requirements change; verify them with current primary documentation.
Next step
Finished reading? Turn the pattern into practice.
Edge or cloud
Read guideAI Readiness and Governance Kit
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