Security for AI: Protect LLMs, RAG Pipelines and AI Agents
Secure and govern the LLMs, RAG pipelines, and AI agents your enterprise builds and adopts across IT and OT.
Protect the in-development and production AI that ordinary security tools were not built to see
Security for AI is Cygeniq's enterprise AI security solution on the Runtime AI Trust Platform. It helps organizations discover sanctioned and shadow AI, test LLMs, RAG pipelines, and AI agents with attacker-style prompts, apply runtime AI security guardrails, monitor AI-native attacks, quantify risk, and produce audit-ready evidence across IT and OT.
Security for AI protects AI in development and production that ordinary security tools were not built to see. It keeps a live AI bill of materials for every model, agent, and copilot you run, including sanctioned and shadow AI across IT and OT. Each system is tested with attacker-style prompts, unsafe prompts and outputs are blocked while the system is running, and the evidence an auditor or board asks for is produced on demand. Prompt injection, jailbreaks, poisoned retrieval, and misused agent tools are caught early, not after an incident.
Cygeniq delivers this Security for AI capability through Hexashield AI, GRCortex AI, and CyberTix AI on one Runtime AI Trust Platform, so discovery, testing, runtime protection, governance, attack tracing, and evidence are connected rather than managed as separate point solutions.
Why Traditional Security Controls Miss AI Risk
Your AI is in production. The controls around it were built for ordinary software.
Copilots, RAG pipelines, and agents that can call tools and APIs are already running in most enterprises. They are attacked through language: prompt injection, jailbreak chains, poisoned retrieval, tool abuse, agents doing more than they should, and crafted inputs that mislead inspection-vision and operational AI models. A SIEM, a GRC register, and a penetration test do not see any of it, so the tools meant to catch risk miss this AI layer entirely.
Three reasons the AI security coverage gap persists
No complete AI inventory
Nobody has a current inventory of the AI in the estate, so risk is discussed only for the systems people happen to remember. Shadow AI discovery is therefore a core part of the security baseline.
No owner
Where a list exists, entries rarely name an owner, a data type, or how bad it would be if that system misbehaved, so no one is accountable for a specific model on a specific day.
No runtime proof
A test done once cannot cover a system whose behavior changes with a model update, a prompt edit, or a new document added to the corpus.
What Security for AI Covers Across the AI Lifecycle
Six jobs across the life of an AI system, on one platform.
Whether your team calls it AI security, GenAI security, LLM security, AI model security, or AI security posture management, the job is the same: know what AI you run, test it like an attacker would, enforce policy while it runs, and prove all of it on demand. AI workload security and AI application security are part of the same picture.
See
A live AI bill of materials covering every model, agent, prompt flow, corpus, connector, and API, sanctioned or shadow, each with an owner, data type, and risk tier.
Govern
Write each control once, then map it to the rules that apply. Assessments run against live evidence, not a questionnaire.
Measure
A live risk score for each AI system, built from real signals: exposure, what red teaming got through, guardrail activity, and blast radius. Scores roll up to the board.
Mitigate
Attacker-style prompt campaigns built for your own systems, followed by runtime guardrails on prompts, output, and agent actions. Tests rerun on every change, and production models are watched for drift from validated behavior.
Monitor
See and trace AI-native attacks in the running estate, from the injected prompt through the agent action to the effect, inside the SOC you already run.
Prove
Red-team results, guardrail events, and risk scores saved as time-stamped evidence, so an audit or a board question is a quick lookup, not a project.
How Security for AI Works: From AI Discovery to Runtime Protection and Evidence
Find the AI
Build the AI bill of materials across sanctioned and shadow systems, IT and OT, each with an owner, data type, and risk tier. This creates the foundation for shadow AI discovery and continuous AI security posture management.
Test it like an attacker
Run prompt campaigns against your own LLMs, RAG pipelines, and agents for prompt injection, jailbreaks, poisoned retrieval, and tool abuse.
Guard it at runtime
Check prompts, output, and agent actions live in production, and rerun the tests automatically whenever the model, prompt, corpus, or tools change.
Score and prove it
Roll a live risk score up from each system to the business unit to the board, trace attacks into your existing SOC, and generate audit-ready evidence.
Find → Test → Guard → Score → Prove
Framework alignment: controls and tests line up with the EU AI Act, NIS2, ISO/IEC 42001, IEC 62443, the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications, the OWASP Top 10 for Agentic Applications, and MITRE ATLAS, so the results hold up with a regulator or a board.
Why SIEM, GRC and Point-in-Time Testing Miss the AI Layer
A SIEM, a GRC platform, and a pen test each cover part of it.
| Capability | Security for AI | SIEM / XDR alone | GRC platform alone | Pen test (point in time) |
|---|---|---|---|---|
| AI inventory | Live AI bill of materials with owner, data type, and risk tier | Not tracked | Static list, updated by hand | Snapshot on test day |
| Attacks covered | Prompt injection, jailbreaks, poisoned retrieval, and tool abuse | Network and endpoint events, not language attacks | Policy mapping, no testing | Only what the tester tried |
| Runtime blocking | Guardrails on prompts, output, and agent actions | Alerts only, after the fact | None | None after the report |
| Risk score | Live score per system, up to the board | Alert counts | Ratings by hand | One findings list |
| Evidence | Time-stamped, linked to the runtime event | Log storage | Gathered before an audit | Goes stale quickly |
Security for AI Outcomes by Stakeholder
One set of evidence, read differently by each role.
Board
One AI risk position for the company, trending over time, with the big exposures named and owned.
CRO
AI risk in the risk register like any other category: measured, tiered, owned, with thresholds that trigger action.
CISO
A live view of the AI attack surface: what was tested, what got through, what the guardrails stop, and where an attack is happening now.
Head of AI / Data
A path to production that does not stall: testing before launch, a clear risk tier, and evidence produced automatically.
Audit and Compliance
Evidence linked back to the runtime event and mapped to every framework in scope, so one control covers several rules.
Plant / OT Security
Control-adjacent AI, including inspection vision, predictive maintenance, and digital twins, inventoried and guarded under the same controls, with OT monitoring integration.
The Cygeniq Products Behind Security for AI
Three products, one Runtime AI Trust Platform.
Security for AI runs on the Cygeniq Runtime AI Trust Platform. Hexashield AI, GRCortex AI, and CyberTix AI share one inventory, one risk score, and one evidence store, connecting AI discovery, protection, governance, and AI-native attack detection.
Hexashield AI
AI discovery and AI BOM, red teaming, runtime guardrails, and drift monitoring for LLMs, RAG systems, and AI agents.
Hexashield AI, the AI red teaming platform
GRCortex AI
AI governance, risk, and compliance, including the risk register, control mapping, monitoring, and audit evidence.
GRCortex AI, the AI governance platform
CyberTix AI
AI-native cyber defense for detecting and tracing AI attacks inside your existing SOC.
CyberTix AI, AI threat detection and responseFrequently Asked Questions
What is Security for AI?
Security for AI means securing and governing the AI an enterprise builds and adopts, including LLMs, RAG pipelines, and agents that can call tools and APIs. It covers finding every AI system, testing it with attacker-style prompts, guarding it while it runs, and proving the controls work. It exists because these systems are attacked through language, and a SIEM, a GRC register, and a pen test do not see that.
How do you secure AI systems?
Start with a current list of every model, agent, prompt flow, corpus, connector, and API, each with an owner, data type, and risk tier. Test your own systems for prompt injection, jailbreaks, poisoned retrieval, and tool abuse. Put guardrails on prompts, output, and agent actions in production. Retest automatically whenever the model, prompt, corpus, or tools change, and save the results as evidence.
What security controls do LLMs and AI agents need?
Input and output checks for prompt injection and unsafe content, limits on what an agent's tools and APIs can do, checks that retrieved content has not been poisoned or gone stale, continuous attacker-style testing that reruns on every change, a live risk score per system, and evidence mapped to the OWASP LLM and Agentic Top 10, the EU AI Act, NIS2, ISO/IEC 42001, IEC 62443, the NIST AI RMF, and MITRE ATLAS.
How is Security for AI different from a standalone guardrail tool?
A guardrail tool checks prompts and output at runtime but does not tell you what AI you have, whether it was tested, how exposed it is, or how to prove it to an auditor. Security for AI adds the inventory, the testing, the risk score, attack tracing in the SOC, and linked evidence on one platform.
What is shadow AI discovery?
Shadow AI discovery identifies AI systems that are running in the enterprise but are not part of the current sanctioned inventory. Security for AI includes sanctioned and shadow models, agents, copilots, prompt flows, corpora, connectors, and APIs in a live AI bill of materials with an owner, data type, and risk tier.
How does Security for AI support AI security posture management?
Security for AI supports AI security posture management by combining a live AI inventory, continuous adversarial testing, runtime guardrails, live risk scoring, AI-native attack tracing, and time-stamped evidence. The result is a current security posture for each AI system that can roll up from the asset level to the business unit and the board.
See It Run Against Your Own AI Estate
Book a walkthrough of Security for AI: an AI bill of materials built from your systems, attacker-style testing against your own models and agents, runtime guardrails, and audit-ready evidence. Scoped assessment, live demo environment, findings you can reproduce.
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