One Foundation.
Applied Where the Requirement Is.

A security program is only as durable as the accountability structure underneath it. Tools get purchased, controls get configured, and then a key person leaves and nobody can say who owns the exception that was granted eighteen months ago or why.

We build the governance layer that makes a security program survivable: named ownership for every control family, documented risk decisions with the authority that made them, defined system boundaries, and an evidence trail that does not depend on institutional memory.

This is the work that makes every framework below easier, because CMMC, RMF, and FedRAMP are all asking variations of the same questions about who decided what, and on what basis.

CMMC requirements are appearing in DoD solicitations now. For most contractors the practical question is not whether to pursue certification but how much work stands between the current state and a defensible assessment, and how long that work takes.

GiaMetrics practitioners hold Cyber AB credentials as Registered Practitioner and Certified CMMC Professional. We support organizations from an initial gap assessment through control implementation, documentation, and readiness for a C3PAO assessment.

The work covers the full NIST SP 800-171 control set, the System Security Plan and Plan of Action and Milestones that an assessor will actually read, SPRS scoring, and the CUI scoping decisions that determine how large and expensive the assessment boundary becomes.

Scoping deserves particular attention. Organizations routinely bring far more of their environment into the assessment boundary than the contract requires, which multiplies both the implementation cost and the ongoing maintenance burden.

RMF is a lifecycle, not a document package, though the deliverable that reaches the Authorizing Official is what most organizations focus on. The categorization decision made in the first step determines the control baseline, the assessment effort, and the cost of every year that follows.

GiaMetrics brings direct experience from the government side of this process. Our founder served as Information Assurance Program Manager at PEO EIS, where roughly 70 percent of the organization’s Authorizations to Operate carried his signature before reaching the Approving Authority, spanning the transition from DITSCAP through DIACAP to RMF.

That perspective matters because an authorization package is read by someone whose job is to accept risk on behalf of the government. Packages that anticipate what that person needs to see move faster than packages that document everything and organize nothing.

FedRAMP authorization is among the more demanding compliance efforts a cloud service provider will undertake, and the difficulty is rarely the controls themselves. It is the sustained coordination across engineering, security, documentation, the assessment organization, and the sponsoring agency over a period measured in quarters.

GiaMetrics brings direct experience at the highest impact levels. Our founder led the effort that obtained the first IL5 FedRAMP authorization issued by DISA, which at the time made his employer only the second cloud service provider authorized to handle all levels of unclassified DoD data.

We support providers pursuing authorization and agencies evaluating cloud services against their own risk posture, including the DoD Cloud Computing Security Requirements Guide impact levels that sit above the baseline FedRAMP requirements.

Every compliance framework eventually asks the same question in different vocabulary: where did this come from, who is accountable for it, and why do you trust it. Organizations that have done real data governance work answer that question quickly. Organizations that have not spend the assessment window reconstructing an answer.

The distinction that matters most is between discovery and understanding. Discovery establishes what data exists and where it lives. Understanding establishes what it means, what it is fit to support, and what it is not fit to support. Most programs skip the second step entirely and then wonder why their governance produces documents rather than confidence.

We build data governance programs that establish named ownership rather than implied ownership, lineage that survives a system migration, provenance that holds up when the origin is questioned, stewardship with actual day-to-day responsibility attached, and explicit statements of intended use.

This work is the foundation for enterprise reporting, for compliance evidence, for records management, and for any analytical or automated system that will consume the data downstream.

Organizations need governance whether or not they ever deploy AI. When they do pursue AI, that same governance foundation becomes essential to AI readiness, because the ownership, lineage, quality, and accountability structures that satisfy CMMC and RMF are precisely what make an AI system trustworthy.

GiaMetrics brings more than eight years of hands-on work in this area, including NIST AI RMF implementation, AI systems integration, and the governance structures that determine whether an automated system can be relied upon for a decision that matters.

The practical work is less exotic than the discourse suggests. It is model inventory and ownership, training data provenance, intended use and prohibited use statements, human oversight and escalation authority, performance and drift monitoring, and a documented basis for deciding that a system is fit to support a given decision.

That is why the approach produces reliable AI as an outcome. Not because AI is the destination, but because a system built on data that is owned, understood, and monitored is a system whose outputs can be defended.