Build AI that maps cleanly to NIST 800-53 from day one.

Yes, and the distinction matters. FISMA is the underlying law that requires every federal information system to implement NIST 800-53 controls, manage risk, and maintain an authorization to operate. FedRAMP is a standardized program that applies FISMA to cloud services so one authorization can be reused across agencies. If your AI runs inside an agency-owned system, you are governed by FISMA directly. If it runs as a cloud offering sold to multiple agencies, FedRAMP is typically the path. Both paths use the same NIST 800-53 control catalog, so the underlying security work overlaps significantly.

Most of them, honestly. The control families that get the most attention on AI work are AC (access control), AU (audit and accountability), CM (configuration management), SI (system and information integrity), RA (risk assessment), and SA (system and services acquisition). AI-specific concerns tend to cluster around CM (how do you track model versions and training data), AU (can you reconstruct why the model made a given decision), and SI (how do you detect drift, poisoning, or degraded outputs). We map the AI components to the right controls up front so your SSP reflects reality rather than wishful thinking.

Continuous monitoring for AI means more than vulnerability scans and log aggregation. You need model performance telemetry, drift detection against a known baseline, input and output logging for audit reconstruction, and alerting when behavior deviates from what your SSP describes. We build those signals into the architecture so they feed the same ConMon reporting your FISMA program already runs. The authorizing official sees one coherent picture, not a separate AI dashboard bolted on the side.

NIST 800-53 is a security baseline. The NIST AI RMF (AI 100-1) covers the things 800-53 does not: fairness, explainability, validity and reliability of model outputs, and the governance structure around how AI is used and retired. OMB M-24-10 and agency-specific AI directives layer on top of both. We help agencies map AI RMF functions (Govern, Map, Measure, Manage) to existing 800-53 control inheritance so you are not running two parallel compliance programs that never reconcile.

Yes, and this is where a lot of AI projects get caught off guard. A model retrain, a prompt template change, or a swap to a new base model all count as configuration changes that need to flow through your CM process. Depending on impact, some changes are routine and some trigger a significant change review with the authorizing official. We help teams define the change thresholds up front so routine retrains do not require a full reauthorization but material behavior changes do not sneak through unnoticed.

FIPS 199 forces you to categorize the system by the impact of a confidentiality, integrity, or availability failure. That categorization (Low, Moderate, or High) drives which 800-53 baseline applies and how much rigor every control needs. For AI, integrity usually dominates the conversation because a model that silently returns wrong answers is an integrity failure at machine speed. We use the categorization to decide whether open-weight self-hosting, an authorized commercial LLM, or a hybrid architecture is the right call for your data and your mission impact.

Three differences. First, we ship working systems, not 200-page assessment decks. Second, every engagement is led by senior engineers with 25+ years of enterprise IT and direct compliance experience, not pyramid-staffed with juniors billing federal rates. Third, we have shipped Human-AI products in production ourselves, so we know where AI breaks under real authorization constraints. If you need a thick deliverable that sits on a shelf, we are not your firm. If you need AI that maps cleanly to 800-53 and actually runs, we are.