AI Security Assessment

Know how your AI application
actually holds up.

A structured, hands-on assessment of your AI application's attack surface—from architecture and data flow to agent permissions and cloud identity boundaries.

The full attack path, from prompt to cloud.

Most AI security reviews stop at the model layer. The incidents that matter—the ones that expose sensitive data or let an attacker move laterally into cloud infrastructure—happen because of what the AI can reach. This assessment covers the whole chain: prompt injection, retrieval abuse, agent over-permission, cloud IAM boundaries, and data exposure.

What we test.

Architecture

AI Architecture Review

Map the full system: LLM, agents, tools, APIs, retrieval pipeline, data sources, and cloud services. Understand the trust boundaries before any testing begins.

  • Architecture diagram and attack-surface map
  • Trust boundary identification
  • Data flow and retrieval analysis
  • Tool and API exposure inventory
LLM & Agents

LLM and Agent Testing

Hands-on testing of the model layer and any agents or tools it controls. Focus on what an attacker can cause the system to do, not just what it reveals.

  • Direct and indirect prompt injection
  • System prompt extraction and bypass
  • Excessive agency and tool abuse
  • Agent permission boundary testing
  • Sensitive information disclosure
RAG & Data

RAG and Data Security

Retrieval-augmented systems introduce data-access boundaries the application must enforce. Test whether they do.

  • Document permission review
  • Cross-user/tenant data exposure testing
  • RAG poisoning scenarios
  • Malicious document injection
  • Excessive retrieval and metadata leakage
Cloud & IAM

Cloud Identity Review

AI agents that can reach cloud APIs, storage, or databases must have scoped, least-privilege identities. This review confirms they do—or identifies where they don't.

  • Service identity and role review
  • API and tool permission scoping
  • Cloud resource access boundaries
  • Credential storage and rotation review
Governance

AI Governance Gap

Technical controls matter, but so does the governance layer: policies, risk classification, third-party model risk, logging, and monitoring.

  • AI inventory and risk classification
  • Third-party model and API risk review
  • Logging and monitoring coverage
  • NIST AI RMF alignment (where applicable)
Deliverables

What you receive

  • Architecture diagram and attack-surface map
  • Technical findings with proof-of-concept detail
  • Risk register with severity and impact
  • Executive report
  • Prioritized remediation roadmap
  • Retest option following remediation

How the assessment runs.

1

Discovery

Architecture review, questionnaire, and access scoping. Map the full system and agree on test targets before any testing begins.

2

Testing

Hands-on assessment across the agreed scope: LLM, agents, RAG, cloud IAM, and governance. Real testing, not just review of documentation.

3

Report & Roadmap

Technical findings, risk register, executive summary, and a prioritized remediation roadmap delivered in writing with a readout walkthrough.

4

Remediation Support

Optional. Direct implementation support on cloud IAM, agent configuration, data-permission controls, and policy changes found during the assessment.

5

Retest

Confirm that remediations hold. Retest the identified findings and deliver a close-out report documenting what changed.

Ongoing

Quarterly Reassessment

AI applications evolve. Quarterly assessments keep the risk picture current as models, agents, and integrations change.

Submit an intake and I'll follow up within one business day.

No pricing on the intake form. Once I understand your environment and scope, I'll send a proposal directly.

Start the Intake →