Purpose-built adversarial agents — trained on real offensive tradecraft under the DARPA CASTLE program — run continuously against your environment and produce dated, auditable proof of exactly where your defenses hold and where they don't.
Your security tools measure activity, not effectiveness, what's running, not whether it would stop a real attack. In government and enterprise, that proof isn't optional: compliance mandates it, auditors demand it, executives need it.
SIEGE runs real adversarial campaigns against your environment at machine speed and shows you exactly where your defenses hold or break.
SIEGE was developed under the DARPA Castle program and proven across U.S. Government and DoD networks. This is adversarial validation with a pedigree, not a compliance checkbox. Pass, and you earn a seal that means your defenses held against the people who write the playbook.
Get a clear, board-ready picture of where your security really stands, in about three minutes, no pen test required.
Direct answers on how SIEGE works, how it differs from alternatives, and what to expect.
Adversarial Exposure Validation (AEV) is a continuous, evidence-based discipline that uses autonomous agents to safely run real attack techniques against your environment and prove which defenses hold. Unlike vulnerability scanning or compliance audits, AEV produces dated, reproducible proof of whether an attacker can reach your crown jewels — before a real adversary does.
BAS tools replay scripted attack libraries against isolated sensors. SIEGE runs autonomous, AI-adaptive agents across your live environment, chaining real techniques the way a skilled adversary does. Developed under the DARPA CASTLE program and validated on DoD networks, SIEGE produces a proven attack path with remediation evidence — not a dashboard of simulated hits.
Yes. SIEGE is designed from the ground up to be non-disruptive. Its agents observe and chain exposures the way an attacker maps a network — they do not execute destructive payloads, crash services, or exfiltrate real data. The same safety constraints validated on DoD operational networks apply to every commercial deployment.
SIEGE is a continuous validation layer, not a full replacement for every penetration test. Traditional pen tests produce a point-in-time snapshot; SIEGE runs continuously so your validation is never more than hours old. Most teams use SIEGE to prioritize what a human red team should focus on, and to prove fixes hold after remediation.
SIEGE maps every finding to the relevant control framework — CMMC Level 2/3, DORA TLPT, HIPAA Security Rule, NIST CSF 2.0, and others — and produces audit-ready evidence packages. This converts a compliance checkbox into dated proof that specific controls are functioning, satisfying examiner expectations for continuous monitoring across these frameworks.
SIEGE was built and iterated inside real DoD and intelligence community networks under the DARPA CASTLE program — not inspired by military concepts after the fact. The tradecraft, agent logic, and safety constraints were pressure-tested on operational infrastructure where mistakes have real consequences. 'Military-grade' is a marketing claim; DARPA CASTLE validation is a contract of record.
No. SIEGE uses specialized neural networks trained through reinforcement learning on real offensive cyber tradecraft — not general-purpose language models. This is a structural difference, not a marketing one. LLM-based security tools generate plausible-sounding attack analysis by reasoning about what an attacker might do. SIEGE's agents do what an attacker does: probe your actual environment, chain real techniques, and return findings that are proven paths, not predictions. Because the intelligence is embedded in the agents themselves, SIEGE runs on commodity hardware with no continuous inference cost — a distinction that matters at enterprise scale.
Book a working session and watch SIEGE run against an environment like yours.