FortiphAI
FortiphAI
A cybersecurity company building evidence-driven RMF execution software for regulated environments.
The FortiphAI platform turns live system telemetry into a maintained compliance picture and package-ready ATO artifacts, with grounded assistance, traceable evidence, and human review built into the workflow.
Evidence-driven RMF execution
Continuous compliance state
Package-ready ATO artifacts
Controlled deployment support
Inputs
SCAP, Nessus, logs, manual evidence, telemetry
Operating posture
Human-reviewed drafts, runbooks, and validation
Deployment fit
Controlled, isolated, and air-gapped environments
Why FortiphAI
Compliance execution should follow the evidence, not the folder structure.
Regulated teams need more than another dashboard or document generator. FortiphAI positions the platform as an operating path that connects evidence, state, review, remediation, and ATO output.
01
Live evidence becomes maintained state
The platform keeps RMF work tied to current boundary, inventory, telemetry, and supporting records so teams are not rebuilding posture from stale package fragments.
02
Maintained state becomes reviewable output
Artifact workflows stay downstream of traceable evidence and current control posture, with human review before anything is published.
03
Guided action stays under operator control
Runbooks and scoped validation help teams move from finding to follow-through without turning remediation into opaque autonomy.
The Problem
RMF work breaks when evidence and artifacts drift apart.
Compliance sprawl is not just inconvenience. It creates review risk: teams lose the connection between what changed, what was validated, what evidence supports the control, and what finally appears in the package.
Evidence, control status, POA&M work, and artifacts spread across separate tools.
One maintained operating picture from evidence intake to package-ready output.
Periodic document reconstruction becomes the center of RMF execution.
Current system context remains the basis for controls, drafts, and review.
Broad automation is difficult to govern, explain, and validate after change.
Human-in-the-loop assistance and selected-target runbooks keep operators in control.
Canonical Pipeline
One defensible path from evidence to artifact.
FortiphAI describes this publicly as a canonical evidence-to-artifact path: a high-level operating model that keeps interpretation deterministic, traceable, and downstream of current system context.
Step 01
Evidence
Scans, logs, telemetry, manual evidence, and supporting records enter a controlled path.
Step 02
Normalize
A canonical normalization spine supports consistent interpretation.
Step 03
State
Control posture, boundary context, and inventory truth stay connected.
Step 04
Review
Assisted drafts, findings, and remediation context remain human-reviewed.
Step 05
Package
ATO artifacts are prepared from traceable evidence and maintained compliance state.
Platform Capabilities
Built around the work regulated teams actually have to finish.
The public story is deliberately simple: FortiphAI gives teams a maintained source of truth for RMF work and supports the evaluation, deployment, and adoption needed to use it in controlled environments.
Compliance and boundary truth
Maintain the relationship between authorization boundary, inventory, control posture, evidence coverage, and readiness.
Artifact and deliverable workflows
Support SSP, SCTM, PPSM, POA&M, diagrams, benchmark reports, and package review from current context.
Grounded assistance and runbooks
Use human-in-the-loop AI assistance and operator-controlled runbooks for failed controls, drafting, review, and scoped validation.
Outputs
ATO package work stays connected to current state.
Drafts and deliverables are framed as evidence-backed, reviewer-controlled, and package-ready. The platform assists the work; FortiphAI keeps the deployment conversation grounded in security, privacy, and operational fit.
SSP
SCTM
PPSM
POA&M
Boundary diagrams
Benchmark reports
Deployment and Trust
A company-side path for evaluation, deployment, and adoption.
FortiphAI can support organizations evaluating the platform for controlled environments where evidence handling, reviewer control, local deployment posture, and security coordination all matter.
Controlled deployment models
FortiphAI supports evaluation, deployment, and adoption planning for regulated environments with deliberate rollout needs.
Air-gapped capable posture
The platform is positioned for environments where RMF execution cannot depend on always-connected external tooling.
Customer-controlled AI options
Policy, provenance, local control, and U.S.-based open-source or open-weight model options can be addressed during deployment planning.
FortiphAI
Bring evidence-driven RMF execution into a controlled environment.
Start with a focused briefing on mission fit, boundary context, package workflows, deployment posture, and how FortiphAI can support your RMF and ATO work.
Contact
contact@fortiph.aiSecurity, privacy, evaluation, and deployment questions are handled directly through FortiphAI.