Securing AI agents for the enterprise
97% of enterprises expect an AI agent security incident within 12 months. Your agent fleet is expanding faster than your security team can audit it. We bring dedicated AI red teaming, threat modeling, and compliance-mapped assessments — so you ship agents that meet NIST, EU AI Act, and internal governance requirements.
Enterprise AI agent security challenges
Traditional application security doesn't cover the attack surface of autonomous AI agents. Enterprises face unique risks that generic pentesting misses.
Agent fleet visibility gaps
Teams deploy agents across customer support, code generation, data analysis, and internal ops. Without centralized security assessment, each agent is a blind spot — and attackers only need one entry point.
Tool-use and MCP attack surface
Agents that call APIs, read databases, or use Model Context Protocol servers inherit the permissions of every connected system. A single tool-poisoning attack can escalate from a chatbot to your production database.
Prompt injection at scale
Enterprise agents process untrusted inputs from customers, partners, and third-party data sources. Every input channel is a potential injection vector — and HackerOne reports a 540% year-over-year increase in AI vulnerability disclosures.
Non-deterministic behavior
Unlike traditional software, agents can take different execution paths on the same input. Security testing must cover not just known attack patterns but emergent behaviors that surface only under adversarial conditions.
Compliance-ready from day one
Our assessments map directly to the frameworks your compliance team requires. Every finding includes a compliance reference so remediation doubles as audit preparation.
NIST AI RMF
Findings mapped to NIST AI Risk Management Framework functions: Govern, Map, Measure, Manage. Audit-ready documentation that satisfies federal and enterprise procurement requirements.
EU AI Act
Risk classification assessment, conformity gap analysis, and technical documentation aligned with EU AI Act requirements. Particularly relevant for high-risk AI system deployments in EU markets.
OWASP Agentic Top 10
Every assessment covers the OWASP Top 10 for AI Agents with concrete test cases and pass/fail criteria per risk category.
Engagement models
Flexible engagements that match your security maturity and agent deployment stage.
Point Assessment
One-time deep-dive into a specific agent or agent system. Red teaming, threat modeling, and compliance gap analysis delivered as an audit-ready report.
Typical timeline: 1–2 weeks
Fleet Assessment
Systematic security review across your entire agent portfolio. Prioritized risk register, cross-agent vulnerability patterns, and organization-wide remediation roadmap.
Typical timeline: 4–6 weeks
Continuous Monitoring
Ongoing red teaming integrated into your CI/CD pipeline. Automated adversarial testing on every agent deployment, with monthly executive reporting.
Engagement: quarterly retainer
How enterprises secure their AI agents
Self-assessment: we red-teamed our own agent first
Before offering assessments to customers, we ran our automated red-teaming pipeline against our own agent. 2 critical findings, 1 high — in a system we built ourselves. Read the full write-up to see exactly what we found and how we fixed it.
Read the case study →Enterprise case studies with client permission coming soon. Get in touch for references.
Secure your AI agents before an attacker does
Enterprise assessments start with a scoping call. We'll identify your highest-risk agents and recommend the right engagement model.
Request Enterprise AssessmentPart of the AI Vyuh portfolio. Also see: AI Code QA · AI FinOps