MachineGhost builds a living digital replica of your system, maps every RMF control, and deploys autonomous AI agents that continuously discover, validate, and verify your security posture.
MachineGhost constructs a cyber digital twin from your system's architecture, authorization boundaries, and control implementation — then feeds it continuous scan data to reflect ground truth.
Automatically maps your system architecture to NIST 800-53 control families. Every component is tagged, categorized, and linked to its responsible controls.
Ingest results from Nessus, ACAS, SCAP, STIG checkers, and custom scanners. MachineGhost normalizes findings and updates your twin in real time.
Move beyond point-in-time assessments. Your digital twin maintains a living Body of Evidence that evolves with every scan, patch, and configuration change.
Autonomous agents validate every control family. Identify drift, contradictions, and inherited risk before assessors do. Proof, not probability.
When AI agents surface validated findings, MachineGhost auto-generates POA&M entries with evidence chains, suggests mitigations, and tracks remediation through closure.
Generate ISSO/ISSM-ready packages on demand. System Security Plans, control narratives, and evidence artifacts — all backed by live twindata.
MachineGhost deploys thousands of focused, short-lived AI agents that autonomously assess your digital twin. CreativeAI discovers — deterministic logic validates. Only proven findings enter your RMF pipeline.
Analyzes the digital twin, identifies priorities, decomposes scope into agent objectives
Thousands of independent agents explore in parallel — each fresh, focused, no bias accumulation
Agents produce findings with structured evidence, reasoning traces, and confidence scores
Logic-based validation confirms exploitability. Rejects noise. Only proven issues survive
Validated findings enter the official Finding → POA&M → Remediation workflow
Verifies implementations are effective — catches contradictions, weak statements, and stale assessments
Discovers stale scan coverage, unmonitored attack surfaces, and concentrated risk on high-value targets
Checks system configurations against STIG baselines and identifies systematic hardening gaps
Identifies missing required controls, weak implementations, and per-family compliance drift
Maps multi-component exploit chains, lateral movement paths, and single points of failure
MachineGhost's AI ingests your architecture, builds a graph-based digital twin, connects scanners, and deploys autonomous agents to continuously validate your posture.
Import system diagrams, CONOPS docs, and existing SSP artifacts. MachineGhost parses components and boundaries automatically.
AI constructs a graph-based digital twin mapping every component to its RMF controls, data flows, and trust boundaries.
Integrate with Nessus, ACAS, SCAP, STIG Viewer, Splunk, and custom telemetry sources via API or file ingestion.
Autonomous agents continuously assess your twin — validating controls, discovering gaps, and verifying every finding with deterministic proof.
Every control family is tracked, scored, and updated as new scan data arrives. See exactly where you stand across all 20 control families — no spreadsheets required.
MachineGhost maps findings to specific controls, calculates implementation percentages, and surfaces the controls most at risk so your team can focus where it matters.
MachineGhost was created by cybersecurity practitioners and systems engineers who lived the pain of manual RMF compliance. We built the tool we wished we had.
Every control status reflects verified scan data — not guesswork or stale assessments.
Full audit trails from scan finding to control status. Every state change is traceable.
Replace manual evidence collection with continuous, machine-driven assurance.
Built for DoD, IC, and federal environments where security is not optional.
Ready to build a living digital twin of your cyber posture? Get in touch for a demo tailored to your authorization boundary.