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A city should not deploy an AI agent until it can identify the agent, limit what it can see and do, obtain accountable human approval for consequential actions, and reconstruct every important decision from logs. Use the checklist below in inventories, risk reviews, solicitations, contracts, testing, operations, and retirement. It applies to stand-alone assistants as well as agents hidden inside permitting, case-management, contact-center, productivity, and public-safety software.
1. Build a living inventory and define the purpose
Start with an inventory that covers every AI agent used by the municipality, including functionality bundled into a vendor application. CISA’s 2023–2024 AI roadmap calls for an agency use-case inventory that can be collected, reviewed, and prioritized; a city-focused inventory from the City AI Governance Project provides a practical municipal format.
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Record the minimum facts
- Department owner and a named operational and technical contact.
- The specific business purpose, expected benefit, affected residents, and geographic scope.
- Model, provider, version, hosting arrangement, and all connected vendors or subprocessors.
- Data categories handled, sources, sensitivity, retention period, and deletion method.
- User groups, authentication method, languages, accessibility features, and service hours.
- Tools, APIs, databases, files, browsers, messaging systems, or physical systems the agent can reach.
- Whether it only recommends, drafts, retrieves, or takes an external action—and what action that is.
- Dependencies, change history, planned end date, and the owner of residual risk.
Update the record when the model, prompt, data source, tool, vendor, purpose, or permission changes. An inventory that only lists pilots will miss the embedded agents most likely to escape review.
2. Assign a risk tier before approval
Risk should reflect the harm from an error, unfair output, disclosure, outage, or unauthorized action—not the marketing label attached to the product. A spelling assistant and a benefits-eligibility agent may use similar language models but require radically different controls.
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High-impact uses
Escalate any use affecting benefits, housing, employment, policing, health, education, immigration, legal rights, or access to essential services. Require a written statement of expected benefits, a documented risk assessment, data-quality review, representative testing, bias mitigation, human oversight, and continuing monitoring. A human reviewer must have enough authority, time, information, and training to reject the agent’s recommendation; a nominal click-through is not meaningful oversight.
Lower-risk does not mean no-risk
Internal drafting, translation, or search may be lower impact, but it can still leak confidential records, reproduce discriminatory language, or create a public-records problem. Apply baseline identity, privacy, logging, accessibility, and incident controls to every tier, then add safeguards in proportion to potential harm.
Federal AI principles describe applications as safe, secure, resilient, understandable, traceable, regularly monitored, transparent, and accountable. Treat those principles as design requirements and document where a local law, records schedule, labor rule, accessibility obligation, or sector regulation imposes a stricter requirement.
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For every field, record the lawful authority and operational purpose for collecting, sending, or retaining it. If a field is not necessary to deliver the approved function, remove it rather than relying on a vendor’s promise to ignore it.
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Data-handling requirements
- Minimize collection and prohibit unnecessary sensitive, biometric, health, financial, immigration, or children’s data.
- Keep production records separate from prompts, evaluation sets, demonstrations, and developer testing.
- Define retention and deletion periods for prompts, outputs, embeddings, backups, support tickets, and telemetry.
- Require confidentiality, no secondary use, and no model training on municipal data unless the city expressly authorizes it in writing.
- Require a current list of subprocessors, processing locations, cross-border transfers, and material changes to them.
- Specify breach notice deadlines, investigation cooperation, evidence preservation, and resident-notification responsibilities.
- Require return or verifiable deletion of municipal data at termination, subject to legal holds and records law.
Separate tenant data cryptographically or through an equivalently documented isolation design. Ask whether support personnel can view prompts, whether administrators can export all records, and how the provider prevents one customer’s retrieval index from returning another customer’s content. Procurement guidance from CMS and federal privacy materials emphasize confidentiality, personally identifiable information, records, and negotiated terms; make those terms enforceable rather than leaving them in a security questionnaire.
4. Give the agent a controlled identity and permissions
An agent must never operate as an anonymous shared account or inherit a human administrator’s unrestricted session. Give the agent, each service, and each tool call an authenticated identity that can be disabled independently. NIST’s AI Agent Standards Initiative identifies agent identity and authorization as an active standards need, so record the design and revisit it as standards mature.
Least-privilege design
- Use short-lived, narrowly scoped credentials and rotate or revoke them automatically.
- Allow only the APIs, records, fields, destinations, and hours required for the approved purpose.
- Segment networks and place sensitive systems behind a policy enforcement point.
- Apply rate limits, transaction limits, anomaly detection, and destination allowlists.
- Validate every tool argument server-side; never trust a value generated by the model.
Require approval for consequential actions
Place an explicit, recorded human approval gate before the agent sends a legal notice, changes an official record, approves or disburses a payment, alters access rights, makes an eligibility decision, or contacts a resident in a way that creates an obligation. Show the reviewer the proposed action, relevant source data, uncertainty or refusal signals, and the exact scope of the change. A reviewer should be able to edit, reject, or return the action for more information.
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Log the complete chain
Capture the identity, user request or scheduled instruction, system and policy prompts, retrieved data references, tool arguments, tool results, model output, approval or rejection, final action, timestamp, and version identifiers. Protect logs from alteration, restrict access, and map retention to the city’s records schedule. Logging only the final answer cannot show whether an agent saw stale, poisoned, or unauthorized data.
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5. Threat-model prompts, retrieval, and tools
Assume that an agent will encounter hostile or misleading content. Threat-model prompt injection, poisoned data, malicious documents, retrieval leakage, insecure plugins, excessive agency, secret exfiltration, and cross-tenant access. Retrieved text is untrusted input, even when it comes from an internal document repository.
Technical defenses
- Keep system policy and authorization decisions outside the model’s control path.
- Separate instructions from retrieved content and label untrusted text clearly.
- Use output schemas, argument validation, type checks, and policy checks before every tool call.
- Redact credentials, tokens, and unnecessary personal data before sending context to a model.
- Constrain network destinations and block arbitrary file, shell, email, and database access.
- Test attempts to extract records, reveal hidden instructions, bypass approval, or escalate privileges.
- Maintain a tested kill switch that revokes credentials and disables integrations without waiting for a model response.
For essential services, maintain a manual fallback that staff can operate during an outage, suspected compromise, unsafe output, or vendor suspension. Federal AI principles call for resilience against systematic vulnerabilities, adversarial manipulation, and malicious exploitation; resilience includes a workable recovery path, not just a detection alert.
6. Put requirements into procurement and contracts
Use the checklist in the request for proposals or quotations, evaluation rubric, security review, renewal, change order, and amendment. CMS procurement guidance asks whether a purchase involves information security or personally identifiable information and routes the review to security, privacy, data, and program officials. Establish that routing before a department signs a pilot.
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- What model, version, hosting environment, data flows, and external services are involved?
- How is each customer’s data isolated, encrypted, backed up, and deleted?
- Which subprocessors and processing locations are used, and how much notice precedes a change?
- Can the city apply records holds, export complete records, and verify deletion at contract end?
- Are prompts, outputs, feedback, and telemetry used for training or any secondary purpose?
- What audit logs, administrative logs, retention controls, and export formats are provided?
- What are the incident-notice timeline, investigation duties, liability terms, and evidence-preservation process?
- May the city or an independent assessor test the system, including authorization and prompt-injection defenses?
- How are model, prompt, tool, data, and subprocessor changes documented, approved, and rolled back?
- How are accessibility, language coverage, translation quality, and assistive-technology compatibility demonstrated?
- Can the city suspend processing, revoke integrations, export data, and perform an orderly shutdown?
Make unsupported answers a procurement risk rather than an informal promise. Require a named incident contact, service-level commitments for critical defects, and cooperation with public-records, audit, and legal-discovery duties.
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7. Test before launch and monitor in production
Define acceptance thresholds and assign an owner for residual risk before testing begins. Use realistic municipal cases, including multilingual, low-bandwidth, accessibility, exceptional, and adversarial scenarios.
Pre-launch test set
- Accuracy, citation or source grounding, refusal behavior, and uncertainty handling.
- Prompt-injection resistance, malicious-document handling, data-poisoning detection, and privacy leakage.
- Authorization boundaries, approval enforcement, rate limits, destination restrictions, and rollback.
- Bias and disparate outcomes across relevant demographic and language groups.
- Accessibility, plain-language output, translation, latency, availability, and safe failure.
- Recovery after a tool outage, corrupted index, revoked credential, model error, or vendor incident.
Record test data, configuration, model version, results, exceptions, remediation, and the approving official. Do not launch merely because average accuracy looks acceptable if a single unsafe action can cause irreversible harm.
Operational monitoring
Monitor drift, incidents, unusual tool use, failed approvals, repeated refusals, complaints, latency, outages, and disparate outcomes. Sample outputs under a documented privacy-preserving procedure. Alert on access outside normal purpose, spikes in exports, unexpected destinations, and attempts to disable controls. Preserve enough evidence for an auditor to reconstruct what the agent saw and did.
The NIST AI Risk Management Framework Playbook organizes suggested actions under Govern, Map, Measure, and Manage and includes monitoring, documentation, privacy, and decommissioning activities. It is voluntary and explicitly not a complete checklist; adapt it to local law and the agent’s actual permissions.
8. Be transparent and provide resident recourse
Publish an understandable description of each public-facing agent’s purpose, data categories, provider, limitations, human-review process, and complaint route, while protecting information that lawfully must remain confidential. Identify automated or generated communications where that disclosure helps residents understand how to respond.
Human correction and appeal
Give residents a reachable human channel to correct inaccurate data, challenge an automated recommendation, and appeal a consequential decision. Tell staff when they must pause an automated workflow and escalate. Do not force a resident to prove that an algorithm was wrong before receiving human review.
Records and accountability
Retain prompts, outputs, approvals, and action logs according to the city’s records schedule and legal holds. Train operators on acceptable use, privacy, accessibility, security reporting, and how to recognize fabricated answers or prompt attacks. Transparency is not a substitute for access controls, but it lets residents and auditors understand the system’s role and limits.
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9. Prepare incident response and retirement
Incident playbook
Define severity levels, notification paths, evidence preservation, credential revocation, containment, rollback, resident notice, and post-incident review before an incident. Decide who can invoke the kill switch and who restores service. Include privacy, security, legal, records, communications, accessibility, and program officials in exercises.
Reassess material changes
Repeat the risk review after a model, tool, data source, vendor, subprocessor, geography, or purpose changes. A model upgrade can alter refusal behavior and data exposure even when the user interface is unchanged.
Retire safely
- Disable service accounts, tokens, webhooks, scheduled jobs, and integrations.
- Export records needed for operations, audits, legal holds, and public-records duties.
- Remove indexes, caches, backups, and test copies according to contract and law.
- Obtain deletion evidence or document the legal reason data remains.
- Notify users, update public documentation, and provide the manual replacement process.
- Record the retirement decision, date, owner, residual data, and lessons learned.
How to compare two municipal agents
Evaluate competing systems on the same evidence, not on demonstrations alone.
Quick Recap
| Evaluation axis | Questions to document |
|---|---|
| Data use and retention | What enters prompts or indexes, where is it processed, how long is it kept, and is it used for training? |
| Identity and least privilege | Can every agent and tool call be authenticated, scoped, rotated, and revoked independently? |
| Tool permissions and approvals | Which actions are possible, which require human approval, and are controls enforced outside the model? |
| Audit-log quality | Can the city reconstruct instructions, retrieved data, tool calls, approvals, outputs, and final actions? |
| Testing and response | Does the vendor support independent testing, incident investigation, rollback, and evidence preservation? |
| Accessibility and language | Are interfaces and outputs usable with assistive technology and in the languages residents need? |
| Contract terms | Are subprocessors, locations, deletion, records holds, breach notice, changes, and shutdown rights enforceable? |
| Explainability and recourse | Can staff explain a recommendation and can residents obtain timely human correction or appeal? |
| Interoperability and portability | Can data, prompts, logs, configurations, and taxonomies be exported in usable formats? |
| Lifecycle cost and suspension | What staffing, testing, monitoring, storage, migration, and retirement work is required, and how quickly can service stop? |
Implementation sequence for a city team
- Inventory all agents and appoint department, privacy, security, records, procurement, and program owners.
- Assign a risk tier and prohibit production use until required approvals are documented.
- Map data flows, lawful authority, retention, subprocessors, identities, tools, and approval gates.
- Write the controls and vendor questions into the solicitation or contract.
- Run adversarial, privacy, bias, accessibility, authorization, and recovery tests against realistic cases.
- Approve only when acceptance thresholds are met and residual risk has a named owner.
- Launch with monitoring, resident notice, trained staff, logs, a manual fallback, and a kill switch.
- Review incidents and material changes, then retire the agent with credential revocation, records export, and verified deletion.
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