AI Agent Security Services
Cogensec secures autonomous AI agents end-to-end — adversarial red teaming, the Agentegrity structural-integrity audit, and Argus runtime defense — for AI labs and enterprises deploying agentic AI in production.
What is AI agent security?
AI agent security is the discipline of securing autonomous and semi-autonomous AI systems — LLM-powered agents, copilots, and multi-agent workflows — against adversarial inputs, tool misuse, data exfiltration, and behavioral drift.
Unlike traditional application security, AI agent security has to account for non-deterministic decision-making, emergent multi-step behavior, and trust boundaries that shift every time the model sees new input. The same model can be safe in one context and dangerous in another depending on tools, data, and identities at hand.
Cogensec delivers AI agent security as a single program covering assessment, scoring, and runtime defense — not point tools.
Agentic AI security risks we address.
Prompt injection & jailbreaks
Direct and indirect injection attacks that exfiltrate data or hijack agent goals.
Tool & function-call abuse
Agents tricked into invoking destructive tools or chaining tool calls outside scope.
RAG & memory poisoning
Adversarial documents and persistent memory entries that bias future decisions.
Identity & RBAC drift
Over-privileged service accounts and confused-deputy patterns across agent hops.
Lethal trifecta
Untrusted input + sensitive data access + outbound communication — the combination that turns a small bug into exfiltration.
Agent collusion
Multi-agent systems where one agent's output becomes another's trusted instruction.
Our AI agent security services.
AI agent red teaming
Adversarial assessments against your AI agents — prompt injection, tool abuse, memory poisoning, identity drift, and the lethal-trifecta combinations that bypass guardrails.
Red Team NetworkRuntime defense (Argus)
Policy-as-code runtime defense for agentic AI: every tool call, retrieval, and outbound action evaluated against your security policy in real time.
Argus platformAgentegrity audits
Structural integrity scoring for autonomous AI across four dimensions: Agent Reliability, Behavioral Containment, Risk Integrity, and Control Plane.
Agentegrity frameworkContinuous AI agent monitoring
Ongoing telemetry, drift detection, and incident response for AI agents in production — not a one-off audit.
See how it worksHow to secure AI agents — our methodology.
Threat model the agent
Map tools, data, identities, and trust boundaries — what the agent can touch and on whose behalf.
Red team the system
Adversarial pressure across prompts, retrieval, memory, tools, and human-in-the-loop surfaces. Reproducible, evidence-grade findings.
Score with Agentegrity
Quantitative scoring across Agent Reliability, Behavioral Containment, Risk Integrity, and Control Plane — a single number you can track.
Deploy runtime defense
Argus enforces policy-as-code at runtime so the risks you found stay caught when the system changes.
Research-grade agentic AI security.
- Published research on agent collusion, semantic inversion, and the Agentegrity framework — see Research.
- The Cortex open-model series for cognitive-security research.
- An elite Red Team Network of AI-native and offensive security practitioners.
- Runtime defense via Argus — policy-as-code for agentic systems.
- NVIDIA Inception partner; SOC 2 Type II ready; ISO 27001 compliant.
AI agent security, answered.
- What is AI agent security?
- AI agent security is the discipline of securing autonomous and semi-autonomous AI systems — LLM-powered agents, copilots, and multi-agent workflows — against adversarial inputs, tool misuse, data exfiltration, and behavioral drift. Unlike traditional application security, it has to account for non-deterministic decision-making and emergent multi-step behavior.
- What is agentic AI security?
- Agentic AI security focuses on AI systems that take actions in the world — calling tools, querying databases, sending messages, executing code — not just generating text. The core problem is that the same model can be benign in one context and dangerous in another depending on the inputs it sees, the tools it has, and the data it can reach.
- How do you secure AI agents?
- In four layers: (1) threat-model the agent's tools, data, and identities; (2) red team adversarially against prompt injection, tool abuse, memory poisoning, and identity drift; (3) score structural integrity with a framework like Agentegrity; (4) enforce policy-as-code at runtime so violations are caught when the system changes. Cogensec delivers all four as a single service.
- How is AI agent security different from LLM security?
- LLM security focuses on the model itself — prompt injection, jailbreaks, output safety. AI agent security has to cover everything LLM security does plus the agent's tools, memory, RAG sources, identity, and multi-step trust boundaries. An agent fails in ways a stateless chat completion cannot.
- What is the best AI agent security platform?
- There is no single best platform — the right answer depends on your stack and threat model. Cogensec's approach combines services (red teaming, audits) with runtime defense (Argus) and a scoring framework (Agentegrity) so you get both the assessment and the controls in one engagement. For research-grade work on frontier systems, that combination is the differentiator.
- Do you offer AI agent security services for enterprises?
- Yes. Cogensec runs paid, scoped engagements for AI labs and enterprises deploying agentic AI in production — from launch-readiness assessments to continuous monitoring programs. Engagements are confidential and matched to your specialty area and stack.
Ready to secure your AI agents?
Confidential scoping. Paid, scoped engagements. Reproducible, evidence-grade findings.
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