Purpose-built cyber intelligence. Not another LLM wrapper.
SIEGE is an AI-driven, purpose-built cyber intelligence platform developed over three years through DARPA CASTLE. It delivers adaptive adversary behavior with customer control and commodity CPU economics.
Pedigree built before the pitch.
SIEGE is a purpose-built cyber intelligence platform developed over three years through DARPA CASTLE. Its reinforcement-learning agents were forged to emulate state-sponsored threats and have been validated across operational Department of Defense environments.
That pedigree isn't marketing language. It is development and operational validation a generic AI wrapper does not gain from a prompt layer. SIEGE validates attack paths in its digital twin, then produces dated, reproducible evidence from authorized execution.
The Standard of Provenance
Where your validation tool came from determines whether you can trust what it tells you, a 10-page brief on the divide between tools built commercial-first and validation born of federal research, with the 5-question litmus test for vetting any vendor before you sign.
10-page technical brief · No email required
Proof, not posture.
When SIEGE assesses your environment and confirms your defenses are sound, that finding carries real weight, not because of what we say, but because of where SIEGE was built and who validated it. The SIEGE Seal of Approval means your security posture has been tested against the same adversarial AI standard used by the U.S. Department of Defense. For your board, your auditors, your regulators, and your customers, there is no more credible statement of resilience. It's not a report. It's proof, backed by the most demanding cyber programs in the world.
Purpose-built cyber intelligence.
Not another LLM wrapper.
An LLM wrapper reasons about what an attacker might do. SIEGE's specialized agents adapt to your environment and prove real attack paths. The distinction is structural. It determines whether findings are reproducible, whether costs scale on CPUs, and whether every action can be governed and audited.
The agents at SIEGE's core are not generic language models. They are specialized neural networks trained through reinforcement learning on real offensive tradecraft. They don't generate plausible-sounding analysis by reasoning about what an attacker might do. They act.
Every environment is unique. SIEGE's agents read your specific topology, adapt to your defenses, and uncover novel attack paths. There is no playbook being replayed and no scripted sequence a defender can memorize. The behavior is autonomous and environment-specific.
SIEGE builds a lightweight digital twin and validates attack paths safely in simulation first. It pivots to emulation or live execution only after explicit human authorization.
Every action SIEGE takes runs through a control plane you define. Scope, constraints, tools, escalation thresholds, and human-in-the-loop approval gates govern agent activity. You get a dated audit record of what the AI did and why.
SIEGE vs. the alternatives.
| Capability | Typical pen test | Typical BAS | Typical validation tools | SIEGE |
|---|---|---|---|---|
| Purpose-built reinforcement learning | ✗ | ✗ | ✗ | ✓ |
| Adaptive adversary behavior | ✓ | ✗ | Partial | ✓ |
| Safe digital-twin validation | ✗ | Partial | Partial | ✓ |
| Customer-governed Control Plane | ✗ | ✗ | Partial | ✓ |
| Zero continuous LLM inference cost | ✓ | ✓ | ✗ | ✓ |
| Commodity CPU economics | — | ✓ | ✗ | ✓ |
| Continuous operation | ✗ | Partial | Partial | ✓ |
| Three years of DARPA development | ✗ | ✗ | ✗ | ✓ |
Representative category comparison; capabilities vary by provider and implementation.