AI & AGENT SECURITY

Give AI systems useful authority without giving them unchecked access.

Review LLM applications, AI agents, RAG workflows, MCP and tool integrations, memory, identity, data access, approval gates, monitoring, and high-impact actions.

Discuss your scope Meet the team

Review surface

What we review

An AI workflow review path testing a tool and data boundary before an external action
  1. 01 Instructions & prompts System instructions, prompt boundaries, injection paths, and control assumptions.
  2. 02 Retrieval & data Sources, access, poisoning, sensitive data, provenance, and isolation.
  3. 03 Memory & state Persistence, cross-user leakage, manipulation, retention, and deletion.
  4. 04 Tools & permissions APIs, MCP servers, credentials, scope, delegation, and dangerous actions.
  5. 05 Identity & approval User, agent, service, and human-in-the-loop authorization.
  6. 06 Monitoring & response Logs, anomaly signals, rollback, containment, and investigation.

Timing

Best used when

Sensitive access

An agent can read sensitive data or call business-critical tools.

High-impact action

Automation can change records, move value, deploy code, or trigger workflows.

New trust model

RAG, memory, MCP, or multi-agent architecture changes authority boundaries.

Before expansion

Leadership needs a bounded view of AI risk before launch or expansion.

Process

From authority mapping to contained agent behavior.

  1. 01

    Map agent authority

    Document models, prompts, retrieval, memory, identities, data sources, tools, approvals, and high-impact actions.

  2. 02

    Trace control boundaries

    Follow untrusted input through reasoning, data access, tool calls, human approval, monitoring, and recovery.

  3. 03

    Test abuse and failure

    Challenge prompt boundaries, permissions, data handling, tool use, and autonomous behavior with realistic scenarios.

  4. 04

    Contain and verify

    Prioritize permission, approval, monitoring, and recovery changes, then verify agreed controls.

Outputs / What you receive

Clear findings, practical fixes, and a report your team can use.

Authority map

AI system, identity, data, tool, and approval boundaries.

Prioritized findings

Technical and workflow findings with abuse and failure scenarios.

Control recommendations

Permission, approval, monitoring, containment, and recovery improvements.

Typical engagement team

Who typically leads this work

The exact team depends on the scope. Every engagement has a principal who owns it from scoping through delivery, joined by the specialists the system calls for, and whoever is assigned is named in your proposal.

José C. Ramírez portrait

José C. Ramírez

Specialist Advisor

Security engineer and trainer with around 10 years across application and protocol security. At ZKsync, reviewed Solidity, account abstraction, and Rust, then built AI-assisted vulnerability-analysis workflows.

Michal Bajor portrait

Michal Bajor

Specialist Advisor

Secured funding, custody, APIs, and on-chain systems at Kraken. Has reviewed 60+ Web3 projects across DeFi, L1s, bridges, oracles, and other critical infrastructure.

Timur Güvenkaya portrait

Timur Güvenkaya

Founder & Partner

Led a security engineering practice for Rust and non-EVM systems across Substrate and NEAR. Earlier, built vulnerability-detection engines at Invicti used by Fortune 50 and public-sector organizations.

Meet the full team

Related services

Secure Code ReviewsFind security flaws in critical code where automated scanners and checklist reviews stop.Secure Architecture & Process DesignDesign or challenge critical systems, workflows, integrations, and operating controls before implementation or major change.Risk AssessmentGive leadership a prioritized view of where security risk concentrates and what to address first.

Next step

Map the authority before the agent uses it.

Share the architecture, models, data sources, tools, identities, and high-impact actions. We will define the right review or design scope.

Discuss your scope