AI Risk Management Framework
NIST
NIST provides a lifecycle approach for evaluating context, evidence, risks, vendors, monitoring, and retirement.
It shapes the tool comparison, pilot, monitoring, approval, and exit criteria.

Compare tools against a real task, local requirements, privacy, accessibility, evidence, cost, and the ability to leave.
Begin with your goal, place, access, time, budget, and responsibility. Open the original sources, compare realistic options, and confirm details with the people who maintain them.
What task—not product name—starts the search?
What information would the tool receive?
What evidence shows it works for this context?
Can people use it accessibly and leave with their data?
Who reviews outputs, handles incidents, and owns the decision?
These original and authoritative destinations are annotated so readers know what each contributes, what still requires local confirmation, and when to use it.
NIST
NIST provides a lifecycle approach for evaluating context, evidence, risks, vendors, monitoring, and retirement.
It shapes the tool comparison, pilot, monitoring, approval, and exit criteria.
Cybersecurity and Infrastructure Security Agency
CISA advises people not to put sensitive information into AI tools, warns about AI-enabled deception, and emphasizes account security and human expertise.
It supports the input restrictions, security controls, synthetic-media rules, training expectations, and incident reporting steps.
U.S. Department of Justice
DOJ identifies common digital barriers and explains the importance of equal access to online information and services.
It makes accessibility and an equivalent alternative required comparison criteria.
Sources and requirements can change. Check the publication date, read the original material, and confirm the rules that apply locally before making a consequential decision.
Compare purpose, evidence, local access, cost, privacy, accessibility, support, and current source information before choosing a next step.
Useful for drafting, questions, and transformation, but account terms, data controls, source support, accessibility, and output reliability vary.
Check age, student data, pedagogy, citation behavior, academic expectations, accessibility, and institutional approval—not only features.
Review training and content rights, provenance, licensing, security, code or media validation, and the ability to export work.
Test a bounded use against a baseline, document total cost and integration, require incident terms, and plan how work continues if the tool changes or ends.
✓ Starting from a specific learner, organizational, or community goal
✓ Opening original sources and recording the review date
✓ Comparing realistic alternatives before committing time, money, or information
× Treating inclusion as endorsement, approval, or ranking
× Relying on a title or summary without checking the current source
× Choosing a tool or program before considering access, privacy, accessibility, cost, and support
Use the comparison and planning tools while reviewing the original resources linked in the evidence section.
Complete one copy for each serious option, then compare the evidence side by side.
Run the same test before recommending a tool.
Compare two tools and one non-AI approach using the same safe task and measures.
Define a good result before testing.
Use identical non-sensitive input in each option.
Score usefulness, accuracy, accessibility, time, cost, and risk.
Verify claims outside the tools.
Recommend proceed, revise, or stop with a written reason.
Did the most impressive demo produce the most trustworthy and practical result?
Share the plan with the people responsible for the work and the people who may be affected. Confirm local rules, protect private information, and name the person who can answer questions or correct a problem.