Agentic AI security
AI agents act on your behalf. Make sure they can’t be turned against you.
Agents now read your email, call your APIs, write code, and move data with real credentials. That makes every document, web page, and tool they touch a potential way in. We help you adopt AI with the same discipline you apply to any other privileged system.
Questions we help you answer
- Which AI agents and tools are running today — sanctioned or not?
- What data and systems can each agent reach?
- Who approved the plugins, connectors, and MCP servers they use?
- What happens if an agent reads a malicious document or web page?
- Can you show customers and auditors that it’s under control?
The risks we test for
Mapped to the OWASP Top 10 for Agentic Applications (2026).
Goal hijack & prompt injection
Hidden instructions in content redirect what an agent is trying to do. ASI01
Tool misuse & code execution
Legitimate tools used in unsafe ways, or generated code run without guardrails. ASI02, ASI05
Identity & privilege abuse
Over-broad credentials reused, escalated, or passed between agents. ASI03
Agentic supply chain
Compromised plugins, prompt templates, models, or external MCP servers. ASI04
Memory & context poisoning
Tampered memory or retrieved context that skews future decisions. ASI06
Multi-agent & trust failures
Unauthenticated agent-to-agent messages, cascading errors, rogue agents, and over-trusting humans. ASI07–ASI10
What we do
A security program for your AI — not a one-time scan.
AI inventory & risk assessment
- Discover sanctioned and shadow AI use
- Classify agents by data access and autonomy
- AI risks added to your risk register
Architecture & threat modeling
- Map agent data flows, tools, and trust boundaries
- Threat model each high-impact workflow
- Prioritized design fixes
Guardrails & least privilege
- Scoped, short-lived credentials per agent
- Human approval for high-impact actions
- Sandboxing, egress controls, and output filtering
Red teaming & testing
- Prompt-injection and jailbreak testing
- Tool-abuse and data-exfiltration scenarios
- Retesting after fixes
AI governance
- AI policy and acceptable-use standards
- Model, vendor, and connector review process
- Management system aligned to ISO/IEC 42001
Monitoring & response
- Agent activity logging and alerting
- Kill switches and credential revocation
- AI-specific incident response playbooks
Frameworks & regulations
Aligned to the standards your customers and regulators reference.
| Framework | What it covers | How we use it |
|---|---|---|
| OWASP Top 10 for Agentic Applications (2026) | The most critical security risks for autonomous AI agents | Threat modeling and red-team test plans |
| OWASP Top 10 for LLM Applications | Prompt injection, data leakage, and other LLM risks | Secure design reviews for LLM features |
| NIST AI RMF (AI 100-1) & Generative AI Profile | Govern, Map, Measure, Manage functions for AI risk | AI risk assessment and governance structure |
| ISO/IEC 42001:2023 | Certifiable AI management system | Policies, roles, and controls ready for certification |
| MITRE ATLAS | Adversary tactics and techniques against AI systems | Detection engineering and attack scenarios |
| EU AI Act | Transparency duties from August 2, 2026; high-risk obligations from December 2, 2027 or August 2, 2028 | Applicability analysis and readiness roadmap |
| HITRUST AI Security Assessment | AI-specific security controls for certification | Readiness for healthcare and vendor assurance |
| CCPA ADMT regulations | Notice, opt-out, and access rights for automated decision-making from January 1, 2027 | Privacy and AI governance working together |
Rolling out AI agents this year?
Let’s review what they can reach and how they could be misused — before an attacker does.