Checklist: Migrating Sensitive Workloads to a FedRAMP-Approved AI Platform

Hook: Why migrating sensitive AI workloads to a FedRAMP platform is non-negotiable in 2026

If you're an IT admin or platform engineer responsible for moving models and sensitive datasets into a SaaS AI platform, you already know the stakes: compliance, auditability, and uninterrupted operations. Manual migration often breaks access controls, loses logging fidelity, and leaves audit gaps — and those gaps are what trigger findings, remediation cycles, or worse, data breaches.

This operational checklist gives you a step-by-step, practical migration plan designed for 2026 realities: zero trust integration, confidential computing, continuous monitoring, and FedRAMP’s expectations for traceable controls. Follow it to migrate models and data while preserving controls, logging, and access management end-to-end.

Top-line: what must be intact after migration

• Access Controls — RBAC/ABAC, MFA, and session controls mapped to agency roles.
• Logging & Audit Trails — Immutable, centralized logs for data access, model inference, and admin actions with retention policies.
• Data Protection — Encryption-in-transit and at-rest, key management, and data segregation.
• Control Mapping — Evidence-ready artifacts mapped to FedRAMP/NIST controls (continuous monitoring, incident response, configuration management).

2026 context and key trends that affect your migration

Late 2025 and early 2026 solidified a few trends that must inform any FedRAMP migration plan:

• Zero Trust as default — Cloud providers and CSPs expect identity-centric controls and microsegmentation when handling Controlled Unclassified Information (CUI).
• Confidential computing and hardware-based enclaves are now common in FedRAMP authorizations for AI workloads to protect model IP and sensitive inference data.
• Continuous Authorization and Monitoring — FedRAMP emphasizes continuous monitoring (ConMon) and automated evidence collection rather than batch audits.
• Model Governance — Expect auditors to ask for model lineage, data provenance, bias checks, and retraining logs as part of system security plans.

Pre-migration checklist: discovery, classification, and risk framing

1. Asset inventory and dependency mapping

List every model, dataset, pipeline, and dependent service. Include container images, third-party libraries, and data stores. Use automation (e.g., dependency scanners, SBOM generators) to create a reproducible inventory.

2. Data classification and marking

Classify datasets as CUI, PII, low-sensitivity, or public. Mark records with tags that travel with data and appear in audit logs. This guides encryption, residency, and retention choices.

3. Map controls to FedRAMP requirements

Create a mapping matrix: each asset → applicable FedRAMP control(s) → evidence artifact(s). Prioritize continuous monitoring controls (e.g., logging, vulnerability scanning, configuration baselines).

4. Risk assessment and scoping

Evaluate risk tolerance and decide scope: full migration, hybrid (on-prem inference), or edge-only for high-risk models. Document residual risks and mitigation plans for reviewers.

Operational migration checklist (step-by-step)

Step A — Prepare the FedRAMP SaaS tenancy

• Obtain SSP (System Security Plan) and PAP (Privacy Assessment Plan) templates from the vendor.
• Ensure the FedRAMP authorization level (Low/Moderate/High) matches the data classification.
• Document network architecture and ingress/egress points, including NAT, proxies, and VPC peering.

Step B — Establish identity and access model

Use federated identity (SAML/OIDC) integrated with your enterprise IdP for centralized provisioning. Implement role-based and attribute-based access controls to enforce least privilege.

// Example AWS IAM role trust for a FedRAMP-authorized account (conceptual)
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": { "AWS": "arn:aws:iam::123456789012:role/EnterpriseAdmin" },
      "Action": "sts:AssumeRole",
      "Condition": { "Bool": { "aws:MultiFactorAuthPresent": "true" } }
    }
  ]
}

• Enforce MFA for all admin roles and for any service accounts with privileged scopes.
• Configure session timeouts, just-in-time elevation, and approval workflows for temporary access.

Step C — Secure key and secrets management

Move cryptographic keys to a FedRAMP-authorized KMS or HSM. Integrate KMS with the SaaS key policy and set CMK rotation and key access logs.

# Example: Terraform snippet to enable key rotation (conceptual)
resource "aws_kms_key" "models_key" {
  description = "KMS key for model artifacts (FedRAMP)"
  enable_key_rotation = true
}

• Ensure secrets (API keys, DB credentials) are stored in a vault with access logs and MFA gating.
• Use envelope encryption to limit plaintext exposure during transfer.

Step D — Data migration and secure transfer

Design a migration pipeline that preserves metadata and auditability. For large datasets, use encrypted, authenticated transfer channels (SCP over TLS, AWS Snowball with encryption, or vendor-provided secure ingestion).

• Hash and checksum all files to verify integrity after transfer.
• Preserve provenance metadata: source, owner, classification, and applied transformations.

Step E — Model migration and versioning

Register models in a versioned model registry. Migrate training artifacts, weights, and config files with their provenance metadata. Record who initiated migration and the reason in audit logs.

• Apply deterministic tagging: model_name:version:hash to avoid ambiguity.
• Store model cards and risk assessments alongside binaries.

Preserving logging & audit trails

Logging strategy

Centralize logs from data ingestion, model training, deployments, and inference. Ensure logs are immutable (WORM) and retained per agency policy. Logs must capture actor, timestamp, action, resource, and outcome.

Example Fluent Bit forwarder config

[OUTPUT]
    Name  es
    Match *
    Host  logs.example-fedramp.ai
    Port  9200
    TLS   On
    TLS.Verify  On

• Enable structured logging (JSON) so SIEM can parse fields for automated evidence collection.
• Forward logs to a FedRAMP-authorized SIEM and set alerts for policy violations and anomalous activity.

Audit evidence and automated collection

Automate evidence collection for: access reviews, patch status, vulnerability scans, configuration baselines, and training records. Use APIs to export attestations to the SSP.

Access control specifics: RBAC, ABAC, and session management

Create resource-centric roles: model-admin, data-owner, inference-operator, and auditor. Use ABAC for nuanced access decisions (e.g., allow inference if user.department == data_owner.department).

// Example ABAC policy (conceptual)
allow if request.user.department == resource.data_owner_department
  and request.action == "inference"
  and request.context.mfa == true

• Rotate service account credentials frequently and prefer short-lived tokens.
• Log all privilege escalations and ensure an approval workflow is recorded.

Testing, validation, and acceptance

Functional and compliance tests

Run unit and integration tests for pipelines and a compliance test-suite that checks controls are effective (e.g., MFA enforcement, encryption checks, logging presence).

Pentest and configuration scanning

Schedule pen testing and automated vulnerability scans after migration. Validate configuration baselines against the SSP and ensure remediation tickets are traceable.

Model-specific validations

• Run model integrity checks (hash verification) and inference regression tests.
• Validate model governance artifacts: model cards, datasource manifests, retraining logs.

Change management and continuous monitoring

Formalize a change control process for model updates, dataset refreshes, and permission changes. Integrate change events into ConMon so auditors can see the timeline of changes and associated approvals.

• Automate drift detection and trigger reviews for retraining or rollback.
• Use feature flags and canary releases for model updates to limit blast radius.

Incident response and forensics

Ensure your FedRAMP provider supports playbooks for data exposure, model theft, or unauthorized inference. Confirm forensic access to logs, snapshots, and encrypted backups.

• Define SLA and RTO/RPO for incident scenarios involving models or datasets.
• Practice tabletop exercises that include the vendor to validate coordinated response.

Operational examples & real-world metrics (experience-driven guidance)

In deployments we’ve observed across government and defense-adjacent teams in late 2025–2026:

• Automating evidence collection cut audit prep time by ~60% when logs, configs, and attestations were centralized via APIs.
• Using hardware-backed enclaves for inference reduced model exposure risk and satisfied high-impact FedRAMP requirements for several agency pilots.
• Implementing ABAC reduced excessive permissions by 45% during one migration, which lowered remediation findings in the initial authorization review.

Common pitfalls and how to avoid them

• Pitfall: Transferring data without metadata. Fix: Use manifest files and update the model registry with provenance.
• Pitfall: Breaking audit trails by using vendor consoles for one-off changes. Fix: Enforce policy-as-code and record approvals via ticketing integration.
• Pitfall: Not validating third-party model components. Fix: Vet third-party models, require SBOMs, and include them in the SSP.

Post-migration: continuous governance and optimization

Migration is not the finish line. Set up these ongoing processes:

1. Weekly automated control checks and monthly manual evidence reviews.
2. Quarterly model governance reviews for bias, performance, and retraining need.
3. Patch and library dependency scanning in CI/CD pipelines with automatic ticket creation for findings.

Quick reference — Practical migration checklist (condensed)

• Inventory assets & generate SBOMs
• Classify datasets and tag provenance
• Map controls to FedRAMP and prepare SSP artifacts
• Set up federated IdP, RBAC/ABAC, and MFA
• Provision authorized KMS/HSM and enable key rotation
• Transfer data via encrypted channels with checksums
• Register models in a versioned registry with model cards
• Centralize logs (WORM, SIEM) and automate evidence collection
• Run pen tests and compliance validation
• Formalize change control and ConMon integration
• Practice incident response with vendor and stakeholders

"Migrating sensitive workloads isn't a one-time lift; it's about baking continuous controls and evidence collection into your delivery pipeline."

Checklist templates and sample artifacts to build now

Before you initiate migration, assemble a package of artifacts you will deliver to the FedRAMP CSP:

• SSP draft with system diagrams and control mappings
• Data classification matrix and transfer manifests
• Model registry exports including model cards and hashes
• Logging pipeline configuration and retention policy
• Change management and incident response playbooks

Final operational tips

• Start small: migrate a single low-risk model first to validate controls and automation.
• Track everything in a single source of truth (ticketing system integrated with the SSP).
• Use policy-as-code so configuration drift triggers automatic remediation or alerts.
• Keep the vendor accountable: require APIs for log access, keys, and evidence export.

Call to action

Ready to migrate with confidence? Download our operational FedRAMP migration template (SSP snippets, logging configs, and model registry schemas) or schedule a free migration readiness review. We’ll map your assets to FedRAMP controls, automate evidence collection, and build a repeatable pipeline so audits become routine instead of disruptive.

Contact describe.cloud to kick off a migration audit and get the condensed checklist and templates you need to start migrating today.