federaldevopssecurity

Preparing an AI Platform for FedRAMP: A Technical Checklist for DevOps Teams

oopensoftware

2026-02-13

10 min read

Concrete technical steps DevOps teams must implement to ready AI platforms for FedRAMP authorization: logging, encryption, topology, and automated evidence.

Preparing an AI Platform for FedRAMP: A Technical Checklist for DevOps Teams

Hook: If you are running an AI platform that must meet FedRAMP authorization, the clock starts the moment a stakeholder says yes. FedRAMP is not just a paperwork exercise. For DevOps teams it becomes a checklist of technical hardening, automated evidence, and operational disciplines that touch logging, encryption, network topology, CI CD, and documentation. This guide gives concrete steps you can execute this quarter to move from prototype to authorized.

Executive summary and the 2026 context

FedRAMP authorization in 2026 places heavier emphasis on continuous monitoring, supply chain risk management, and automation for evidence collection than in earlier cycles. Late 2025 guidance and industry trends pushed assessors to expect:

Automated evidence pipelines that reduce manual audits
FIPS validated cryptography and HSM backed keys for all controlled data in transit and at rest
Comprehensive, structured logs including model inputs and inference outputs when required by data use agreements
Clear separation of duties and zero trust network topology for production AI clusters

Below is a practical, prioritized checklist with configuration examples and operational patterns for DevOps teams preparing an AI platform for FedRAMP.

Priority checklist at a glance

Classify data and map to FedRAMP baseline level: Low, Moderate, or High
Enforce encryption with FIPS validated modules and HSM backed keys
Implement structured, immutable logging and centralized collection
Design a segmented, least privilege deployment topology
Automate evidence collection via IaC, CI CD hooks, and audit pipelines
Produce a complete System Security Plan and POA&M aligned to controls
Engage a 3PAO early and run readiness scans and pentests

1. Data classification and control scoping

Before technical work, decide the FedRAMP impact level. This determines the control set and mandatory protections. Most AI platforms that host controlled unclassified information target Moderate or High. Document the data flows and tag data at ingestion so enforcement policies work end to end.

Actionable steps

Create a data flow map that shows ingestion, model training, model storage, inference, export, and observability sinks
Tag datasets at source with classifications that your pipeline understands
Define retention and redaction rules for logs and model artifacts

2. Encryption: keys, algorithms, and operational rules

FedRAMP requires FIPS compliant cryptographic modules for eligible controls. In 2026 you must demonstrate key custody, rotation policies, and separation between platform and customer keys for multi tenant deployments.

Technical requirements

Use envelope encryption: protect data with data keys, protect data keys with KMS or HSM
Store keys in FIPS 140 validated HSMs or cloud KMS with FIPS endpoints
Enable TLS 1.2 minimum, prefer TLS 1.3 for internal and external connections
Maintain key rotation policy and automated rotation logs

Example: envelope encryption pattern

1. Request data key from KMS or HSM
2. Encrypt payload with data key using AES GCM
3. Store encrypted payload and encrypted data key together
4. Decrypt by retrieving encrypted data key and asking KMS to decrypt

For cloud implementations use FIPS validated services or dedicated HSM appliances. For example consider using dedicated HSM clusters for private keys and AWS KMS custom key store or Azure Dedicated HSM to meet FedRAMP High expectations.

3. Logging: immutable, structured, and evidence-ready

FedRAMP assessors expect logs that show who did what and when. For AI platforms that means you must capture operational telemetry plus model observability logs where applicable.

What to log

Authentication and authorization events across identity providers
API requests and responses with metadata, not raw PII
Model training jobs: dataset id, commit hash, hyperparameters, model artifact id
Inference events: model id, timestamp, request metadata, decision id (avoid storing raw PII unless justified)
Key management events: create, rotate, revoke
Configuration changes and IaC apply events

Formatting and retention

Emit logs in structured JSON with a consistent schema
Sign or hash logs and ship to an immutable store for tamper evidence
Define retention based on classification and legal requirements and ensure automated deletion

Logging pipeline example

# example flow
services -> OpenTelemetry -> Collector -> Centralized SIEM
SIEM -> Immutable cold storage with object lock

Use OpenTelemetry for consistent instrumentation. Back the collector with a resilient delivery path to your SIEM. In 2026 many assessors expect that you can produce log slices on demand via automated queries rather than manual searches. Consider automating metadata extraction and log annotations so evidence queries are reproducible and machine readable.

4. Deployment topology and network controls

FedRAMP wants strong network separation, limited blast radius, and least privilege. AI platforms tend to be complex with GPU clusters, data lake storage, and inference endpoints. Design your topology so each concern is isolated and auditable.

Recommended topology

Management plane: isolated management VPC or network, accessible only via bastion and MFA backed admin paths
Training plane: private subnets with dedicated GPU node pools, no external egress except via controlled data egress gateways
Inference plane: public or private endpoints behind WAF and API gateways with strict rate limiting
Observability plane: separate network segment for logging, metrics, and tracing with write only paths to central SIEM

Network controls to implement

Network ACLs and Kubernetes network policies for pod level isolation
Private endpoints for storage and KMS
DNS filtering and egress proxy for allowed outbound traffic
Service mesh or mTLS to enforce service to service authentication

Kubernetes hardening examples

# enforce least privilege with RBAC and pod security admission
apiVersion v1
kind Pod
metadata name example
spec
  securityContext runAsNonRoot true

Use OPA Gatekeeper or Kyverno for policy as code. In 2026 assessors expect admission controls to be part of the automated evidence collection. If you’re adopting distributed or edge-hosted inference, review hybrid edge workflows and edge-first patterns for recommendations on low-latency, provable network boundaries.

5. CI CD and Infrastructure as Code for automated evidence

FedRAMP reviews require artifacts: build logs, deployment approvals, IaC templates, and test results. Automate their generation and storage.

Practical steps

Source control everything: IaC, configs, pipeline definitions, test harnesses
Generate signed build artifacts and keep artifact hashes in immutable storage
Enforce signed commits and pipeline approvals for production deploys
Attach CI CD outputs to SSP evidence automatically via webhooks

Example evidence flow

# pipeline steps
1. unit and security tests
2. static analysis and SBOM generation
3. build and sign artifact
4. store artifact hash in immutable evidence bucket
5. deploy to staging after manual approval
6. automated test results appended to evidence store

Tools such as inSpec, OpenSCAP, and SBOM generators are commonly used. Capture all scan outputs and link them to control IDs in your SSP. If you operate a modular cloud stack or composable cloud architecture, ensure your IaC references clearly map modules to control implementations.

6. Documentation and evidence: SSP, POA&M, and runbooks

Documentation is central to FedRAMP. The System Security Plan must clearly map your architecture and controls to FedRAMP control IDs. In 2026 assessors expect machine readable pointers and automated links from artifacts to the SSP.

Must have documents and artifacts

System Security Plan (SSP) with control implementations and owners
Plan of Actions and Milestones (POA&M) for gaps and mitigations
Incident Response Plan and forensic procedures for model and data incidents
Configuration baselines and build images with SBOMs
Onboarding and role based access control matrices

Document automation tips

Map IaC resources to SSP control statements using tags and comments
Generate artifact links in the SSP automatically from CI CD pipelines
Update POA&M automatically when scanners produce failures

7. Auditing and continuous monitoring

Continuous monitoring is the backbone of FedRAMP sustainment. Maintain a program that measures compliance continuously and produces evidence without manual intervention.

Monitoring components

Host and container level integrity checks and automated remediation
Configuration drift detection with alerts and auto remediation where safe
Vulnerability scanning and scheduled pentests tied to POA&M
SIEM rule sets that map to FedRAMP logging controls

Automated evidence examples

# example
daily scans -> store results in evidence bucket
alerts -> create incident and attach log slice and code commit
quarterly pentest -> signed report uploaded to evidence system

Make your monitoring outputs auditable and reproducible. Provide assessors the ability to replay key events using preserved logs and artifacts — and keep a documented playbook for evidence replay and responder coordination.

8. AI specific controls: model lineage, inference logging, and privacy

AI introduces new control points that FedRAMP assessors examine closely. Demonstrate how you prevent data leakage, trace model provenance, and limit retention of sensitive inference inputs.

Practical controls

Maintain model registry with model artifacts, hashes, training dataset ids, and approvals
Log inference metadata but redact or hash PII before storage unless authorized
Implement differential privacy or synthetic data when training on sensitive data
Use model explainability only in a controlled environment to avoid leaking data

Lineage example

# model registry record
model id 1234
training dataset commit abcdef
hyperparameters hashed
artifact hash sha256:...
approval signed by security officer

Link model registry entries to SSP control mappings and audit trails. For secure personal data handling at endpoints, evaluate on-device AI patterns that reduce exposure of raw data to centralized stores. Also consider integrating open-source and commercial tools that help detect synthetic inputs and misuse, such as deepfake detection solutions where input authenticity matters.

9. Test, validate, and work with a 3PAO

Bring in a Third Party Assessment Organization early for a pre assessment. Run internal readiness checks, automated scanning, and at least one red team exercise targeted at both infra and model-level attacks.

Test plan checklist

Automated configuration scan and remediation run
Host and container image scanning with SBOM verification
Network penetration and WAF rule testing
Model specific tests for membership inference and data exfiltration scenarios

10. Example timeline and milestones

Sample 16 week plan for a DevOps team preparing for an authorization attempt.

Week 1 2: Data classification, impact level decision, and architecture mapping
Week 3 6: Implement encryption, KMS HSM configuration, and key rotation automation
Week 5 9: Build centralized logging pipeline and immutable storage with retention rules
Week 8 12: Harden cluster topology, apply admission policies, and enforce RBAC
Week 10 14: Automate evidence collection from CI CD and link to SSP
Week 12 16: Internal readiness assessment, 3PAO engagement, and POA&M cleanup

Practical checklists and artifacts to prepare now

System Security Plan draft with architecture diagrams and control mappings
Retention policy, encryption key policy, and incident response playbooks
CI CD proof of signed build artifacts and immutable evidence store access logs
Automated log queries that produce evidence slices for common auditor requests
Model registry with lineage and tamper evidence

Real world advice from teams who achieved authorization: automate everything you can. Manual packaging of evidence is the most common cause of delays in authorization.

Final words and next steps

FedRAMP authorization is a program of continuous improvement. In 2026 the expectation is clear: strong cryptography, immutable and machine readable evidence chains, and demonstrable continuous monitoring. For AI platforms you must extend traditional controls to cover model provenance, inference telemetry, and data handling guarantees.

Start by scoping your data, implementing FIPS validated encryption and HSM key custody, instrumenting structured logs with immutable storage, and automating evidence flows from CI CD to SSP. Partner with a 3PAO early and schedule iterative readiness assessments so your POA&M shrinks over time instead of growing.

Actionable takeaway

Deliver a working evidence pipeline in 8 weeks by automating log collection, storing immutable artifacts, and linking CI CD outputs to the SSP
Provision HSM backed keys and enforce envelope encryption for all controlled data
Register models with a verifiable lineage record and redact PII before long term log storage

Call to action: Ready to operationalize this checklist for your AI platform? Download our FedRAMP AI readiness template and runbook or schedule a 30 minute readiness review with our engineers at opensoftware.cloud. We help DevOps teams convert technical controls into automation, evidence, and authorized systems.

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