Government AI Compliance: Meeting FedRAMP and FISMA Requirements
Government AI programs often start with a practical question that turns into a compliance question very quickly: can a team use AI to summarize tickets, analyze documents, or support internal operations without creating a new uncontrolled system boundary? In federal and federal-adjacent environments, that question is usually framed through FedRAMP, FISMA, NIST SP 800-53, agency ATO expectations, data handling rules, and a clear demand for reviewable evidence. Keeptrusts does not grant a FedRAMP authorization or replace a system security plan, but it can provide a useful governance boundary around model routing, role-based access, data-loss prevention, and audit logging so that an AI workflow is easier to defend during review.
The important distinction is operational. FedRAMP and FISMA are not satisfied by saying a provider is trustworthy in general. Reviewers usually want to see how requests are constrained, how data is kept on approved paths, how access is limited to named roles, and how control evidence can be exported. That is where controls such as RBAC, DLP Filter, Data Routing Policy, and Audit Logger become useful.
Use this page when
- You are deploying AI for government, regulated contractors, or public-sector shared services.
- You need a defensible control story for access, routing, and evidence export under FedRAMP or FISMA review.
- You want to connect AI governance work to existing agency security controls instead of creating an exception path.
Primary audience
- Primary: Technical Leaders
- Secondary: Technical Engineers, compliance reviewers
The problem
Government teams rarely fail because they lack policy language. They fail because AI usage arrives faster than the control implementation around it. An analyst starts using an AI assistant to summarize incident notes. A contractor pastes operating procedures into a model to draft a memo. A program office wants faster triage of support requests. Each use case sounds small, but the control questions accumulate immediately.
Who is allowed to use the route? What data classes are allowed in prompts? Which model endpoints are approved for agency workloads? Can a contractor use the same route as a federal employee? If a reviewer asks for evidence of blocked requests, provider selection, or redaction behavior, is that evidence exportable without reconstructing it from logs spread across multiple tools?
FedRAMP and FISMA reviews are usually broader than the gateway itself. Reviewers care about inherited controls, boundary definitions, incident response, logging, and access management across the full system. But AI governance still needs to map to familiar control families such as AC for access control, AU for audit and accountability, and SC for system and communications protection. If an AI path bypasses those expectations, it becomes difficult to justify in the SSP, in continuous monitoring, or during an authorization package update.
The biggest mistake is treating AI routing as an informal convenience layer. In public-sector environments, that usually leads to undocumented provider use, broad shared access, weak separation between staff and contractors, and very little evidence about what actually reached a model. That posture is hard to defend even when the underlying use case is reasonable.
The solution
The practical solution is to make the AI path look like a controlled service instead of a best-effort helper.
Start with RBAC. Government environments need named roles, required identity headers, and explicit separation of duties. A contractor analyst, system owner, incident responder, and procurement reviewer should not all inherit the same tool rights or data sensitivity allowance. Keeptrusts can enforce that the request carries the right metadata and that the route behavior changes by role.
Add DLP Filter and Data Routing Policy next. DLP is the backstop for sensitive data classes that should not pass through a route at all. Data routing narrows the approved provider set so the route only uses targets whose declared handling requirements match agency expectations such as zero retention, no training reuse, or local-only processing. Missing provider metadata can be treated as non-compliant, which is usually the right behavior for government lanes.
Then make evidence export a first-class requirement rather than an afterthought. Audit Logger plus the export workflows in Export Compliance Evidence give reviewers a concrete answer when they ask how blocked requests, routing decisions, and policy outcomes are recorded. That matters because FedRAMP and FISMA programs are built around repeatable evidence, not one-time assurances.
If the team is still defining boundary language, the surrounding docs are useful anchors: Government, Data Residency, Pass Compliance Audits, and Team-Based Governance. They do not replace agency-specific control mapping, but they help teams translate policy intent into runtime behavior.
Implementation
This example creates a government-only AI route that requires role metadata, blocks sensitive-data leakage, and refuses provider targets that do not declare an approved handling profile.
pack:
name: fedramp-fisma-governance
version: 1.0.0
enabled: true
providers:
targets:
- id: approved-government-target
provider: openai
model: gpt-5.4-mini-mini
secret_key_ref:
env: OPENAI_API_KEY
data_policy:
zero_data_retention: true
training_opt_out: true
retention_days: 0
in_memory_only: true
sanitized: true
allow_internet_egress: false
local_only_processing: true
policies:
chain:
- rbac
- dlp-filter
- data-routing-policy
- audit-logger
policy:
rbac:
deny_if_missing:
- X-Agency-ID
- X-User-ID
- X-User-Role
roles:
system-owner:
allowed_tools:
- search
- summarize
- review-control-family
contractor-analyst:
allowed_tools:
- search
- summarize
dlp-filter:
action: block
data-routing-policy:
require_zero_data_retention: true
require_no_training: true
max_retention_days: 0
require_in_memory_only: true
sanitize_before_provider: true
allow_internet_egress: false
local_only_processing: true
on_no_compliant_provider: block
log_provider_selection: true
audit-logger: {}
The route design is intentionally conservative. It does not claim that every workload is suddenly FedRAMP-ready because a gateway policy exists. It does something more useful: it narrows who can use the route, constrains where the request can go, and preserves evidence about what happened.
The operational validation loop should stay simple and repeatable:
kt policy lint --file ./fedramp-fisma-governance.yaml
kt gateway run --policy-config ./fedramp-fisma-governance.yaml --port 41002
kt events tail --policy rbac
kt events tail --policy data-routing-policy
kt export create --format json --filter "policy=audit-logger,rbac,data-routing-policy"
That gives the team a quick way to prove three things during implementation and review: access checks are enforced, non-compliant providers are excluded, and evidence is available for later inspection.
Results and impact
This pattern makes AI governance legible to government security reviewers. Instead of explaining AI usage as a generic productivity feature, the team can point to role restrictions, provider constraints, and exported decision evidence. That is a stronger position for SSP updates, ATO conversations, and continuous-monitoring reviews.
It also reduces operational drift. Staff routes, contractor routes, and higher-sensitivity review routes can be separated explicitly rather than managed by tribal knowledge. When the provider profile changes, the route can fail closed instead of silently expanding to a target the agency never intended to approve.
Most importantly, the program gets a usable audit trail. Reviewers do not need to accept a verbal claim that the AI path is controlled. They can inspect the recorded behavior.
Key takeaways
- FedRAMP and FISMA governance for AI is mostly about control implementation and evidence, not marketing claims about the model provider.
- Use RBAC to separate staff, contractors, and elevated review roles.
- Use DLP Filter and Data Routing Policy to keep prompts on approved paths.
- Use Audit Logger and export workflows to create reviewable evidence.
- Treat the gateway as part of the control boundary, not as a shortcut around agency security practice.