Context is the new signature.
Signatures catch what has been seen before. CHEETaH™ reads your system's own context to anticipate how an adversary would move through it, then carries that prediction into how you build, approve and defend it.
The SSP, the POA&M and the control narratives are assembled at the end of the cycle from whatever evidence can be found, and go stale the day they're signed.
Exercises run on generic scenarios and end in a readout. The gaps they find rarely become requirements, tickets or detections.
The SIEM ranks alerts by rule severity, with no idea which asset carries the mission or which path an adversary would actually take.
CHEETaH™ gives all three the same source of context, and a prediction of how your system would be attacked.
The Heuristic Predictor is the core engine. Everything else in CHEETaH™ either feeds it context or acts on what it predicts. It works from a graph specific to your environment and forecasts where risk is heading.
CHEETaH™ anticipates the techniques and paths an adversary is most likely to use against your system. Analysts refine them into attack paths, test "assume breach" variants, and export them in the formats your red team already uses.
CHEETaH™ finds the single fixes that break a whole attack path, so effort goes where it stops the most. Each path becomes security requirements with NIST 800-53 controls.
Works with the SIEM/SOAR you already run and re-prioritizes incoming alerts by mission or business impact, with the reason shown for every change.
Made for the people who build, approve and defend a system: engineers and DevSecOps teams, security and compliance leads, and SOC analysts.
The Heuristic Predictor shows the paths an adversary is most likely to take through your system and the single fixes that break them, with a rationale for every finding.
Reads what your pipeline and your network already produce, shows what each change means for security before it ships, and ranks live alerts by what matters most.
Reviews the AI agents, MCP servers and tools in your environment: what they can reach, what they are allowed to do, and where they open new attack paths.
Runs cyber tabletops on predicted attack paths and records what was mitigated, detected or missed, so gaps become tracked work.
Keeps security evidence, requirements and remediation current as the system changes. Supports NIST 800-53 and RMF packages, including eMASS.
Chat on every page: explain a finding, draft a control narrative or walk an attack path.
Agents, MCP servers and the tools they call can read data and take actions on your behalf. CHEETaH™ treats them like any other part of the system: something an adversary can use.
An inventory of the AI agents, MCP servers and tools in your environment, and who owns each one.
The data, systems and actions every agent and tool has access to, and where that is more than it needs.
Agents and tools appear in the same attack path prediction as the rest of your system, with the single fixes that break those paths.
Tabletop scenarios built for AI-era failures: a poisoned pipeline, a prompt injection, a data leak, and an AI answer that is confidently wrong.
The review runs entirely on your own systems. Nothing about your AI setup leaves the building.
The methods behind these are proprietary. We share the detail in a technical brief under NDA.
Behind CHEETaH™ is a dedicated research suite where new prediction methods are developed and tested. It brings current work in applied mathematics and AI, from probability and forecasting to optimization, into the Heuristic Predictor.
New methods are developed in a separate research environment, away from the product you depend on.
Each method has to earn a track record. Its predictions are recorded and scored against what actually happened.
Only methods that prove themselves move into the predictor, so the engine keeps improving without asking you to take it on faith.
Start from attack paths predicted for your system, record what was mitigated, detected or missed as you walk them, and carry every gap into the places where it gets fixed.
The Heuristic Predictor proposes likely attack paths for your system. Pick one or build your own.
Build the path visually, from first foothold to mission effect. Run "assume breach" variants.
Record what was mitigated, detected or missed at each step. CHEETaH™ shows where one fix stops the path.
Requirements, POA&M items and alert priorities come straight from the results. The exercise is saved for next time.
CHEETaH™ works with the pipeline and scanners you already run. Each release gets a clear decision with the reason, and compliance that slips is caught before release, not at the next assessment.
We walk through it in a demo on synthetic data. Request one.
A pilot proves CHEETaH™ on your own system and your own work before you commit to anything larger.
One system, with a defined start and finish.
Criteria are agreed in writing at kickoff, and the final report is measured against them.
It runs where your data already lives.
You keep everything the pilot produces, whether or not you license.
CHEETaH™ stands for Cyber Heuristic Exploitation, Evaluation, Targeting, and Hunting.
CHEETaH™ is developed by Capstone Research Corporation, a Huntsville, Alabama cyber and software company. It was built for defense first, where systems are disconnected and the stakes are highest, and it is entering a funded pilot with a U.S. Department of War customer.
Our focus is predictive analytics for cyber defense: knowing how a system is likely to be attacked before it happens, and turning that into systems that are built securely and defended with foresight. The same approach applies to any organization that has to prove its critical systems are secure.
With local models, nothing leaves your enclave: the model runs on your hardware and CHEETaH™ sends nothing outside it. Cloud frontier models are used only if you approve them, under your own account. Your data is never used to train anything.
No. The release decision is deterministic, and every result carries its reason. Language models help where they add value, such as explaining findings and drafting narratives, and a person reviews before anything is acted on.
No. CHEETaH™ reads the scan results you already produce, works with the SIEM/SOAR you already run, and syncs POA&M items with eMASS. It adds the context and the prediction those tools don't have.
One on-prem server or workstation to start, with a GPU for local AI models. Larger deployments scale to a dedicated server, and we size it with you during the pilot.
Never. Demonstrations use synthetic systems and synthetic scan results. We don't bring customer or CUI data to a demo, and we ask that you don't either.
Tell us a little about your system and we'll set up a demo on synthetic data, or scope a pilot.
