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AI Security & Hardening.

Find where your AI systems and agents can be attacked, then deploy the controls that close it.

The problem

AI systems and agents create attack surfaces that conventional security testing was never built for. Prompt injection, system prompt leakage, excessive agent permissions, and unsafe output handling behave nothing like the bugs a traditional pentest is tuned to find.

An assessment that ends in a report leaves the exposure open. Findings sit in a backlog while the agents keep running. This engagement tests the system, then changes its posture: it deploys enforced policy across the AI traffic that carries real risk.

How the engagement works

Phase 1 - Security assessment

Adversarial testing of your AI systems, agents, and integrations. Every finding is rated by severity and mapped to the OWASP Top 10 for LLM Applications, so the report speaks a framework your auditors and customers already recognize.

Phase 2 - Hardening

Enforced policy is configured inline on AI traffic against the findings: identity- and data-aware rules, tool and MCP allowlists, output controls, and audit. The exposure is closed, not documented.

What’s included

-Architecture review of AI systems, agents, and integrations
-Adversarial testing for prompt injection, jailbreaks, and system prompt leakage
-Agent and tool review: MCP and tool permissions, and the blast radius of each connected tool
-Output handling review for data leakage, unsafe rendering, and PII egress
-API and access control assessment across model endpoints
-Findings mapped to the OWASP Top 10 for LLM Applications
-Inline enforcement of policy on AI traffic
-Identity- and data-aware policies, tool and MCP allowlists, and output controls configured against the findings
-Evidence-grade audit and an organization-wide kill switch enabled

What you get

-Technical findings report with severity ratings, mapped to the OWASP LLM Top 10
-Remediation guidance for each finding
-A hardened configuration: policies, tool allowlists, and identity controls deployed and tuned
-Verification testing that confirms each closed finding
-Executive summary for leadership
-90-minute technical debrief with your engineering and security teams

Who this is for

-Companies building AI products and agent features
-Teams deploying agents with access to tools, data, and external systems
-Security teams preparing for audits or customer due diligence
-Engineering teams that need findings closed, not just listed
Timeline
Assessment 2–4 weeks. Hardening scoped from findings.
Structure
Fixed-fee assessment, scoped hardening

Methodology

1. Scoping

Define targets, the agents and systems in scope, rules of engagement, and escalation contacts. Set the boundary between assessment and hardening.

2. Assessment

Hands-on adversarial testing across architecture, agents, tools, APIs, and output handling. Findings consolidated, rated by severity, and mapped to the OWASP LLM Top 10.

3. Hardening

Deploy and configure enforced policy on AI traffic to close each finding.

4. Verification

Re-test against the original findings to confirm each one is closed, then hand off the live configuration and a technical debrief.

Start with a focused assessment

Two narrower engagements feed into AI Security & Hardening. Either is a lower-commitment way to begin.

Shadow AI Audit

Find the unsanctioned AI tools your employees use, and the data going into them.

OWASP LLM Top 10 Assessment

Test your deployed LLM apps and agents against every risk in the OWASP LLM Top 10.

FAQ

Is this a penetration test?

It goes further. The assessment phase covers AI-specific attack vectors a traditional pentest does not test for: prompt injection, system prompt leakage, excessive agent permissions, and unsafe output handling. The hardening phase then closes the findings by deploying enforced controls, rather than handing back a report.

What does the hardening phase actually change?

Enforced policy is configured inline on AI traffic: identity- and data-aware rules on every model and agent interaction, tool and MCP allowlists scoped per agent, output controls for sensitive data, and evidence-grade audit. It does not require rewriting your application code.

Which AI platforms and models do you cover?

OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, and self-hosted models. The engagement is model-agnostic, and the deployed controls govern AI traffic regardless of provider.

Do you need access to our source code?

No. The assessment runs against running systems, agents, and APIs. Source review can be added when it materially improves coverage.

How is the engagement priced and scheduled?

The assessment phase is fixed-fee and runs two to four weeks. The hardening phase is scoped from the findings, since the work depends on what the assessment surfaces. A 30-minute scoping call sets the boundaries before either phase begins.

Book a 30-minute call to scope an assessment for your AI systems and agents.