Citadel Siege is the autonomous adversary simulation platform that continuously attacks your own environment — testing every control, every detection rule, every response playbook, and every human process — to find the gaps before a real adversary walks through them. Continuous Automated Red Teaming. Breach & Attack Simulation. Purple Team Orchestration. MITRE ATT&CK validation. Running 24/7/365.
You deployed a SIEM. You configured EDR on every endpoint. You wrote 500 detection rules. You built 200 SOAR playbooks. You hired a SOC team. But do any of them actually work? Annual penetration tests check a snapshot. Quarterly vulnerability scans check known CVEs. Neither tests whether your detection stack actually detects, whether your response playbooks actually respond, or whether your analysts actually investigate. Siege answers the question that keeps CISOs awake at 3 AM: are my defenses real, or are they theater?
Siege deploys autonomous AI agents that emulate real-world adversaries — APT groups, ransomware operators, insider threats, and supply chain attackers — executing multi-step attack campaigns against your live environment continuously. Every technique is mapped to MITRE ATT&CK. Every detection is validated. Every gap is documented. Every response is measured. The result is not a report. It is a continuously updated, evidence-based answer to the question: "Can we stop this attack?"
Siege validates your entire security stack — detection, response, and recovery — against real adversary behavior.
Annual penetration tests are snapshots of a moving target. Your environment changes daily — new servers deployed, new users onboarded, new applications installed, new cloud resources provisioned — and each change potentially creates a new attack path. Siege's CART engine deploys autonomous AI agents that continuously explore your attack surface, chain exploits across misconfigurations and vulnerabilities, and discover multi-step attack paths that point-in-time assessments miss. The agents operate within safety guardrails — no destructive actions, no data exfiltration, no production impact — but their reconnaissance, lateral movement, and privilege escalation techniques mirror real adversary behavior.
BAS executes specific attack techniques — ransomware encryption simulation, credential harvesting, C2 communication, data exfiltration, lateral movement — in a safe, controlled manner against production systems. Each simulation validates whether the corresponding detection rule fires, whether the SOAR playbook triggers, and whether the analyst receives an actionable alert. The system records which techniques are detected, which are missed, and which generate false negatives — producing an evidence-based detection coverage map that replaces assumption-based security assessments.
Most organizations claim MITRE ATT&CK coverage based on the detection rules they have written — not on whether those rules actually fire when the technique is executed. Siege tests every claimed detection by executing the corresponding ATT&CK technique and measuring whether the detection triggers. The result is an evidence-based ATT&CK heatmap: green for validated detections, red for gaps, yellow for partial coverage. This heatmap becomes the foundation for detection engineering priorities — ensuring the SOC invests in closing real gaps, not hypothetical ones.
Different adversaries use different techniques. Siege's adversary emulation engine replicates the complete kill chains of 40+ documented threat groups — from initial access through persistence, lateral movement, collection, and exfiltration — using the specific tools, techniques, and procedures documented in threat intelligence reports. When your organization's threat profile indicates APT29 as a primary concern, Siege runs the APT29 campaign against your environment and tells you exactly where that specific adversary would succeed and where your defenses would hold.
Cloud environments create attack surfaces that traditional scanning cannot assess — IAM permission chains that allow cross-account pivots, misconfigured service roles that enable privilege escalation, public S3 buckets that expose sensitive data, and Kubernetes RBAC misconfigurations that allow container escape. Siege maps these cloud-native attack paths by simulating the actions a real attacker would take after gaining initial access to a cloud workload — revealing the blast radius of a single compromised credential or misconfigured role.
Identity is the initial access vector in 80% of breaches — and most organizations have never tested whether their identity infrastructure can actually withstand a determined credential attack. Siege simulates 25+ identity attack techniques: password spraying against Active Directory, Kerberoasting service accounts, DCSync replication, Golden and Silver Ticket forging, Pass-the-Hash and Pass-the-Ticket attacks, MFA fatigue bombing, OAuth consent phishing, and SAML token manipulation. Each simulation validates whether the corresponding identity detection fires and whether the automated response locks down the compromised account.
Organizations are deploying AI agents, LLM copilots, and autonomous systems faster than they can secure them. Siege's AI security testing engine red-teams your deployed AI systems against the OWASP Top 10 for LLMs and the OWASP Top 10 for Agentic Applications (2026): prompt injection (direct and indirect), jailbreaking, data leakage from training data, model theft through API extraction, goal hijacking of autonomous agents, tool misuse in agentic workflows, and supply chain attacks through compromised AI plugins. Each test validates whether your AI guardrails hold under adversarial pressure.
Traditional red and blue teams work in isolation — red discovers gaps, writes a report, throws it over the wall, and blue gets to it eventually. Siege closes that loop in real time. When a simulation identifies a detection gap, the system automatically generates the detection rule needed to close it, tests the rule against the same technique, validates that the new detection fires correctly, and updates the ATT&CK coverage map — all within a single automated workflow. The purple team orchestration engine transforms security validation from a periodic exercise into a continuous, self-improving defense cycle.
We thought our ATT&CK coverage was 89%. We had 500 detection rules, 200 SOAR playbooks, and a team of 14 analysts. We were confident. Siege showed us the truth: 54%. Almost half of our detections had never been validated against actual technique execution. Some rules had syntax errors. Some triggered on the wrong data source. Some had been broken by a SIEM upgrade six months earlier and nobody noticed. The gap between assumed security and validated security is where breaches live. Siege closed that gap.
The LockBit emulation was the most valuable security exercise we have ever conducted. In 90 minutes, Siege showed us exactly how a ransomware operator would move through our environment — and it found 8 paths we had no detection for. Including the backup systems. Our backups were on the same network as production. A real ransomware operator would have encrypted them first. We fixed it in a week. That one finding alone may have saved this hospital system from a catastrophic attack.
The AI red teaming caught something that terrified me. An indirect prompt injection — a hidden instruction embedded in a customer email — could cause our LLM agent to call an internal API and export customer records. The guardrails we had built? They stopped direct injection. But the agent parsed the email content, processed the hidden instruction, and treated it as a legitimate customer request. Without Siege, that vulnerability would have been discovered by an attacker, not by us. We now run AI red teaming daily. Every model update. Every prompt change. Every new tool integration.
Launch a Siege campaign against your environment. Discover what a real adversary would find — before they do.