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A city should not deploy an AI agent until it can answer five questions: what the agent is for, what data it can reach, which actions it can take, who can stop or review it, and how the municipality will investigate a failure. The checklist below turns those questions into procurement requirements, technical controls, testing evidence, resident protections, and a retirement plan.
What a municipal AI-agent checklist must cover
An AI agent is software that can interpret instructions, retrieve information, call tools or services, and sometimes act outside the chat interface. In a municipality, that could mean searching case records, drafting a notice, routing a service request, updating a database, or initiating a transaction. The privacy and security risk comes from the combination of data access and delegated action, not from the language model alone.
Use this checklist for custom agents and for agents hidden inside permitting, customer-service, records, analytics, case-management, or productivity products. Keep it as a living control document: update it when the model, connected tool, data source, vendor, purpose, or permitted action changes.
1. Create a complete, living inventory
Start with an inventory that includes every departmental experiment and every embedded vendor feature. CISA’s artificial-intelligence roadmap calls for an agency use-case inventory that can be collected, reviewed, and prioritized. The City AI Governance Project provides city-oriented inventory and risk-tiering instruments.
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Fields to record for every agent
- Department owner and named technical and business administrators.
- Business purpose, expected public benefit, and the process the agent replaces or assists.
- Model, provider, version, hosting arrangement, and major subcontractors.
- Data sources and fields touched, including records retrieved at run time.
- User groups, authentication method, geography, and affected populations.
- Retention for prompts, retrieved content, outputs, logs, and backups.
- Tools and external systems available to the agent.
- Whether it can send messages, change records, approve or deny something, create a financial obligation, or trigger another workflow.
- Current risk tier, human-review requirement, launch date, and next review date.
Inventory entries should identify agents that can act indirectly. For example, an assistant that only drafts an email may still create risk if a connected workflow automatically sends the draft without a human check.
2. Risk-tier the use before approving it
Classify each use by the consequence of an error, misuse, outage, or discriminatory result. A low-impact drafting assistant and an eligibility-screening system should not pass through the same approval path.
Escalate high-impact uses
Require enhanced review when an agent influences benefits, housing, employment, policing, health, education, immigration, legal rights, or access to essential services. The review should document the expected benefit, affected groups, data-quality risks, testing plan, bias-mitigation measures, human oversight, and monitoring owner.
Suggested approval questions
- What is the worst reasonably foreseeable consequence of a wrong or manipulated output?
- Can a resident lose money, a service, a right, housing, employment, liberty, or access to care?
- Can the decision be explained, corrected, and appealed by a person?
- What happens if the model is unavailable, wrong, or presented with malicious content?
- Is the proposed automation necessary, or would a lower-risk rules-based process meet the need?
Federal AI principles describe trustworthy applications as safe, secure, resilient, understandable, traceable, regularly monitored, transparent, and accountable. Treat those qualities as approval criteria rather than marketing claims.
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3. Control privacy and data handling
For every field the agent can read or generate, document the lawful authority and the specific purpose. Collect only what the task requires. Do not place sensitive records in prompts or test sets merely because the model can accept them.
Required data controls
- Minimize collection and block unnecessary sensitive or identifying fields.
- Separate production records from prompts and documents used for development or testing.
- Define retention and deletion periods for inputs, outputs, conversation histories, embeddings, audit logs, and backups.
- Require confidentiality, restrictions on secondary use, and a clear prohibition or permission regarding model training on municipal data.
- Identify every subprocessor and the location where data is stored, processed, or supported.
- Set breach-notification deadlines, investigation duties, and evidence-preservation requirements.
- Specify how the municipality can retrieve its data and require verified deletion at contract end, subject to legal holds and records law.
Procurement guidance from federal programs emphasizes confidentiality, privacy, data protection, records obligations, and negotiated vendor terms. A vendor’s default online terms are not a substitute for clauses tailored to public records, privacy law, and municipal retention schedules.
4. Give the agent a controlled identity and narrowly scoped permissions
Every agent and every tool call should have an authenticated identity. Do not let an agent operate with a shared administrator account or a human employee’s long-lived credentials.
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- Use least-privilege roles and separate read, draft, submit, approve, and administrative capabilities.
- Issue short-lived credentials and scoped API tokens; rotate and revoke them centrally.
- Segment networks and data stores so a compromised agent cannot move laterally.
- Allowlist approved tools, domains, queues, and destination systems.
- Apply rate limits, transaction limits, and anomaly alerts.
- Require step-up authentication for sensitive operations.
- Record the agent identity, user identity, instruction, retrieved data, tool arguments, result, approval, and final action.
Require a human gate for consequential actions
An explicit human approval should precede sending legal notices, changing official records, approving payments, altering access rights, making eligibility decisions, or contacting a resident in a way that creates an obligation. The approval screen should show the proposed action, supporting data, confidence or uncertainty indicators where available, and the person who approved it. NIST’s AI Agent Standards Initiative identifies agent identity and authorization as an active standards need, so treat these controls as foundational even while standards develop.
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5. Defend prompts, retrieval, and tools
Threat-model the agent as an application exposed to untrusted instructions. Prompt injection can arrive through a resident message, an uploaded document, a web page, a case note, or a poisoned knowledge-base entry. Retrieved text must be treated as data, not as a new policy or administrator command.
Threats to test
- Prompt injection that tells the agent to ignore policy or reveal hidden instructions.
- Data poisoning that changes a reference document or inserts a malicious record.
- Retrieval leakage across residents, departments, or tenants.
- Insecure plugins or connectors that accept unchecked commands.
- Excessive agency, in which a draft-capable agent can also send, delete, approve, or purchase.
- Attempts to exfiltrate secrets, credentials, protected records, or system prompts.
Engineering safeguards
- Validate every tool argument against a strict schema and business rule.
- Constrain destinations, file paths, recipients, query scope, and transaction values.
- Redact secrets and unnecessary personal data before model exposure.
- Keep approval logic outside the model so the model cannot authorize its own action.
- Test cross-tenant and cross-department access with deliberately adversarial inputs.
- Maintain a tested kill switch and a manual fallback for essential services.
Federal principles call for resilience against systematic vulnerabilities, adversarial manipulation, and malicious exploitation. Passing ordinary accuracy tests does not demonstrate that an agent is safe against these attacks.
6. Put the requirements in the solicitation and contract
Include the checklist in an RFP, RFQ, renewal, and amendment review. CMS procurement guidance asks whether information security or personally identifiable information is involved and routes the review to security, privacy, data, and program officials. Use a similar cross-functional route in city government.
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Questions to require in the vendor response
| Area | Evidence to request |
|---|---|
| Architecture and data flow | Model and hosting description; diagrams showing prompts, retrieval, tools, storage, backups, and support access. |
| Tenant isolation | Controls preventing one municipality, department, or resident from seeing another’s data. |
| Subprocessors and location | Current list, processing locations, change notice, and objection or termination rights where appropriate. |
| Records and deletion | Support for retention schedules, legal holds, export, verified deletion, and deletion from derived stores where applicable. |
| Security operations | Audit-log fields, access controls, incident process, notification timing, and evidence-preservation commitments. |
| Testing and assurance | Independent assessments, penetration testing scope, red-team results, remediation tracking, and access for municipal testing. |
| Change management | Notice of model, tool, data, or subcontractor changes; version history; rollback or suspension process. |
| Accessibility and language | Supported languages, accessible interfaces, assistive-technology compatibility, and limitations by channel. |
| Exit and portability | Data export format, transition assistance, credential revocation, integration removal, and orderly shutdown procedures. |
Make material answers contractual. If a vendor will not state whether municipal prompts are used for training, disclose subprocessors, provide logs, or support suspension, treat that as an unresolved risk rather than an assumption.
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7. Test before launch and monitor after release
Build a test set from realistic municipal cases, including edge cases and protected populations. Record acceptance thresholds and name the person who accepts residual risk.
Pre-launch test categories
- Accuracy, uncertainty handling, and refusal of unsupported requests.
- Prompt-injection resistance and malicious-document handling.
- Authorization boundaries, tenant separation, and tool-argument validation.
- Privacy leakage from prompts, retrieval, logs, and error messages.
- Bias and disparate outcomes across relevant groups.
- Accessibility, language quality, latency, availability, and failure recovery.
- Human approval workflow, audit-log completeness, rollback, and manual fallback.
Continuous monitoring
Monitor model or data drift, incidents, unusual tool use, failed approvals, resident complaints, latency, outages, and disparate outcomes. Preserve enough evidence for an auditor or investigator to reconstruct what the agent saw and did: identity, instruction, retrieved context, model output, tool call, approval, and final result. NIST’s AI RMF Playbook organizes suggested actions under Govern, Map, Measure, and Manage and includes monitoring, documentation, privacy, and decommissioning activities. The Playbook is voluntary and explicitly states that it is “neither a checklist nor set of steps to be followed in its entirety”; adapt it to state and local law, public-records duties, labor rules, accessibility requirements, sector regulation, and the agent’s actual permissions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Be transparent and provide resident recourse
Publish an understandable description of each public-facing or high-impact agent. State its purpose, data categories, vendor or service provider, limitations, human-review points, and complaint route, while protecting information that the law requires the city to keep confidential.
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- Retain prompts, outputs, approvals, and action logs under the city’s records schedule.
- Provide a human channel to correct inaccurate information.
- Provide a way to appeal consequential decisions to a qualified person.
- Train staff on escalation, accessibility, privacy, and prohibited uses.
Transparency is not satisfied by saying that “AI may be used.” Residents need to know what the system can do, where human judgment enters, and how to challenge an error.
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9. Prepare incident response and retirement before activation
Incident response runbook
- Define severity levels for privacy exposure, unauthorized action, discriminatory impact, service outage, and suspected compromise.
- Specify notification paths for security, privacy, legal, records, program leadership, affected vendors, and residents where required.
- Preserve prompts, retrieved documents, logs, credentials, outputs, and system state needed for investigation.
- Revoke credentials, disable tools, isolate integrations, and switch to the manual fallback.
- Contain the problem, roll back the model or configuration, and verify that unauthorized access has stopped.
- Assess resident impact, make required notifications, document corrective action, and conduct a post-incident review.
Retirement and major-change review
Reassess the agent after a model, tool, data source, vendor, or purpose change. To retire it, disable credentials, remove integrations, export required records, delete data according to contract and law, and document who approved the decision. Keep the inventory entry and retirement evidence so the city can demonstrate what happened later.
How to compare two municipal agents
Use the same evidence request and scoring scale for both products. A polished demonstration should not outweigh weak controls around data, identity, or exit.
| Comparison axis | What to examine |
|---|---|
| Data use and retention | Purpose limitation, training use, retention periods, deletion scope, locations, and subprocessors. |
| Identity and least privilege | Per-agent identity, scoped credentials, role separation, rotation, revocation, and segmentation. |
| Tool permissions | Allowlisting, argument validation, rate limits, transaction limits, and approval gates. |
| Auditability | Whether logs connect user, prompt, retrieved data, output, tool call, approval, and action. |
| Testing and response | Independent testing, adversarial testing, incident notice, evidence preservation, and kill switch. |
| Accessibility and language | Accessible interfaces, language coverage, channel limitations, and accommodation process. |
| Contract terms | Records holds, confidentiality, deletion, change notice, audit rights, portability, and suspension. |
| Explainability and recourse | Human review, reason codes or supporting evidence, correction route, and appeal process. |
| Interoperability and lifecycle | Export formats, integration dependencies, migration effort, total lifecycle cost, and ease of retirement. |
A practical approval sequence
- Enter the use in the inventory and name the accountable owner.
- Assign a risk tier and route high-impact uses to legal, privacy, security, data, accessibility, and program review.
- Map data flows, permissions, tools, retention, subprocessors, and fallback operations.
- Write approval gates, logging requirements, resident notices, and contract clauses.
- Run the pre-launch test suite with realistic and adversarial cases.
- Record acceptance thresholds, unresolved risks, monitoring metrics, and the stop authority.
- Launch narrowly, review logs and complaints, and expand only after evidence supports the change.
- Reassess on every material change and retire the agent using the documented shutdown plan.
What the evidence does not establish
Authoritative public sources do not provide a dependable municipal AI-agent breach rate, adoption rate, or quantified privacy-outcome figure that can be generalized across cities. Procurement decisions should therefore rely on documented controls, test evidence, contract language, and observed monitoring results rather than an unsupported industry statistic.
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