Continuous generative AI red teaming for LLM applications, RAG pipelines, agentic systems and fine-tuned models.
Generative and agentic AI fail in ways your security stack was never built to see. A traditional security stack was built to catch known vulnerability classes in known types of software. Generative and agentic AI do not fail that way. The failure is in what the model was convinced to do, what it was tricked into revealing, or what an agent was steered into acting on, and none of that shows up in a conventional vulnerability scan. AI security red teaming tests those failure paths directly.
Prompt injection and jailbreaks hijack the model through inputs it was designed to trust, bypassing the guardrails you assumed held because the attack never looks like an attack until it works.
Sensitive context, system prompts and training data can be exposed straight through the model's own output. No breach of the underlying infrastructure is required.
Autonomous tools take real actions, including spending, sending and deploying. An attacker who can steer the input can steer the action. The agent is not malfunctioning. It is doing exactly what it was told.
With no adversarial baseline and no audit trail, the gap between one point-in-time test and the next is where new exposure quietly accumulates, unnoticed until something breaks in production.
Adversarial assurance, delivered as a service.
ARTaaS is Hexashield AI operated as an ongoing AI red team service rather than a one-time engagement. The same adversarial techniques a real attacker would use are run against your AI on a schedule, not just before launch, and every finding comes back with evidence attached, not just a severity rating.
Cygeniq's team thinks like an attacker: injection, jailbreaks, leakage, tool abuse and multi-agent chaining. The team then hands back the evidence, the fixes and the proof, not just a list of theoretical risks.
Assets are retested whenever models, data, prompts or agents change, so assurance holds between releases instead of expiring the moment something in the system is updated.
Findings are prioritized by business impact so remediation engineers know what to fix first, with audit-ready proof the board can use, not a report that sits in a shared drive.
We test every class of AI you run. A chatbot, a RAG pipeline and an autonomous agent do not share an attack surface, so they are not tested with the same playbook. Each asset class gets adversarial techniques built for how it actually fails.
Chatbots, copilots and assistants are tested for prompt injection, jailbreaks, system-prompt leakage and unsafe output handling, the failure modes specific to a model that talks directly to users.
Knowledge-base poisoning, retrieval manipulation, cross-tenant exposure, and grounding and citation failure are tested because a RAG system is only as trustworthy as what it retrieves and how well it keeps that data separated.
Tool and function misuse, excessive agency, memory poisoning, and multi-step and multi-agent abuse chains are tested for the risks that only exist once an AI system can actually take action.
Training-data extraction, backdoors, model theft, and alignment and guardrail bypass are tested to verify whether the model you built is actually as safe as the one you tested before deployment.
Every test is a complete engagement. A red team finding that never gets fixed is not worth much more than the vulnerability it describes. Every ARTaaS engagement runs the full sequence from scoping to a verified fix, not just the testing in the middle.
Asset type and complexity are agreed before any work begins, so the engagement is sized to the actual system, not a generic template.
The threat model is mapped to the actual AI architecture in front of the team, not a generic AI threat list applied regardless of how the system is built.
Cygeniq's red team runs expert-led testing against the application and its guardrails, using the same techniques a real attacker would attempt.
False positives are removed and real risk is confirmed, so the findings that reach you are the ones that actually matter.
Executive and technical findings are delivered together, with fixes prioritized so engineers know exactly what to act on first.
One remediation re-validation is included, confirming the fix actually closed the finding rather than leaving it to chance.
Govern once, standardize and test every asset. The groundwork for how testing is scoped, scored and evidenced happens once. What repeats after that is testing on every individual AI asset, not a fresh methodology negotiation each time something new ships.
The threat-model baseline, attack library, rules of engagement, severity model and evidence standards are set up once at the organizational level, so every later engagement runs against the same consistent bar.
Reusable attack playbooks exist for LLM, RAG and agentic AI because each has a genuinely different attack surface. Testing one with a playbook built for another misses what actually matters.
Scheduled red-team tests run on each AI asset, with reporting and retest included, delivered as an annual subscription so testing keeps pace with how often the asset itself changes.
Transparent pricing: a published rate card by AI asset type and complexity, with volume tiers and a 3-year rate lock. Your estate is classified once at the scoping workshop.
Every finding maps to a standard, and to a fix. A finding that cannot be mapped to a recognized framework is hard for a regulator or a board to act on. Every finding from ARTaaS is mapped to the OWASP Top 10 for LLM Applications, LLM01 through LLM10, OWASP agentic AI risks, RAG and vector risks, and MITRE ATLAS. From there, findings flow straight into CyberTix AI runtime guardrails and GRCortex AI compliance evidence, so the test is the start of the fix, not the end of the engagement.
Findings flow directly into CyberTix AI runtime guardrails for immediate protection.
Findings flow into GRCortex AI compliance evidence for audit-ready proof.
What you get from ARTaaS:
Built on one platform, not a stack of point tools.
Security, runtime defense and governance run on one Runtime AI Trust Platform, not point tools.
Testing, runtime protection and control evidence are combined for always-current assurance.
Fluent across OWASP, MITRE ATLAS, ISO/IEC 42001, NIST AI RMF and the EU AI Act.
Specialists across LLM, RAG and agentic AI, delivered from the Cygeniq AI Delivery Center.
One classification, one rate card and one refresh cycle. The model grows with the AI you run.
Start with a scoping workshop. Cygeniq classifies your AI estate, sizes your testing volume, and selects the first assets for a 90-day pilot, so the first engagement lands on the systems where a real finding would matter most.
Start with a Scoping Workshop
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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