Services # Security services for agentic systems. Five services. Most engagements start with a review of one system and grow from there. If you are not sure which one fits, start with the first. [Get in touch](https://mhl42.ai/#contact) - [01 **Agentic system review** *Assessment*](https://mhl42.ai/services#agentic-review) - [02 **AI infrastructure review** *Assessment*](https://mhl42.ai/services#infrastructure-review) - [03 **Architecture and threat modelling** *Design*](https://mhl42.ai/services#architecture) - [04 **Operational security** *Ongoing*](https://mhl42.ai/services#operations) - [05 **Training** *Workshops*](https://mhl42.ai/services#training) - [· **How we work** *Method*](https://mhl42.ai/services#approach) 01 ## Agentic system review **For teams who** have an agent or agentic workflow in production or close to it, and need to know what it can be made to do. We assess the complete workflow, not the model in isolation: instructions and context, memory and retrieval, the identities and credentials the agent holds, the tools it can call, approval steps, interaction with other agents, and what happens downstream. Where it matters, we demonstrate the attack rather than describe it. The result is a clear picture of the system's real attack surface and a prioritised plan for closing it. ### You get - Threat model of the system - Prioritised findings with evidence - Remediation plan - Optional: test cases for the findings that matter most ### Format Point-in-time review, optionally followed by a re-test. 02 ## AI infrastructure review **For teams who** run the platform that agents depend on: cloud accounts, model gateways, MCP servers, orchestration, CI/CD. We review the platform for the case where a model, tool, integration or operator is compromised, and ask how much authority it inherits. That covers identity and access, secrets, isolation, network paths, logging, cost controls and incident response. The review is useful on its own and pairs well with an agentic system review when the agents and the platform are owned by different teams. ### You get - Trust-boundary analysis - Findings and control recommendations - Logging, detection and response requirements ### Format Point-in-time review of a platform or a defined boundary. 03 ## Architecture and threat modelling **For teams who** are designing or redesigning an agentic system and want the security decisions made while they are still cheap. We work alongside your team to define agent identities, trust boundaries, capability limits, approval paths, isolation and recovery. The threat model uses [TRACE for agentic AI](https://mhl42.ai/research#trace), our specialisation of the TRACE methodology for systems that act through a model. The output is written for the people who will build the system, and it doubles as the test plan for a later review. ### You get - TRACE threat model report - Security requirements and architecture decisions - Abuse cases and a test plan ### Format Design workshop with your team, followed by the written model. 04 ## Operational security **For companies that** use agents internally, in developer workflows or in privileged automation, and need security practices that keep up with them. Support covering access to models and data, onboarding of vendors and tools, release controls for prompts and policies, detection of agent-specific failures, and incident preparation. Available as a one-off assessment or as an ongoing arrangement. ### You get - Assessment of current practice - Policies and controls that fit the way you work - Incident playbooks for agent-specific failures ### Format Focused assessment, or an advisory retainer. 05 ## Training **For teams whose** engineers, security staff or leadership need a working understanding of how agents fail and how to build them safely. Workshops built on our twelve-module agentic AI security course: the new attack surface, zero trust for agents, prompt injection, least privilege, harness and runtime security, secret management, model and supply-chain security, multi-agent systems, and a hardened reference setup. We adapt the material to your systems and your audience. ### You get - Engineering workshops - Security team training - Leadership briefings ### Format Half-day to multi-day, on site or remote. How we work ## Assume the model is compromised. Then look at what it can reach. ![A security consultant maps an agentic system, traces a hostile route across a trust boundary, and turns the finding into a control and a repeatable test.](https://mhl42.ai/assets/trace-method.jpg) *Model the system · Follow the authority · Prove the control* - 01 **Start from the consequence** We begin with what must not happen, then trace which identities, credentials and tools would let an agent make it happen. - 02 **Model the system with TRACE** Threat actors, roles, assets, critical invariants and edges give your team and ours one shared picture of the system. - 03 **Test to the effect** A model saying something odd is not a finding. A boundary being crossed is. We follow attacks through to an observable result. - 04 **Keep the evidence** The findings that matter become tests that run again when the model, the prompts or the tools change. Where it helps your security and governance teams, we map findings to OWASP Agentic AI, MITRE ATLAS and the NIST AI Risk Management Framework. Engagement formats ## How the work is packaged - **Design workshop** Threat model and architecture decisions, worked through with the team. - **Point-in-time review** Assessment of an agent, a product, a platform or a defined boundary. - **Review plus test suite** The review, with the most important findings retained as regression tests. - **Ongoing advisory** Regular time with your team as the system and the threat landscape change. Something else? ## Describe the system. We will suggest the smallest engagement that answers your question. We also take on commissioned research: a focused investigation into an attack class, a protocol or a control pattern. See the [research page](https://mhl42.ai/research#commissioned) for how that works. [Get in touch](https://mhl42.ai/#contact) --- Source: https://mhl42.ai/services Site guide for agents: https://mhl42.ai/llms.txt · Whole site as Markdown: https://mhl42.ai/llms-full.txt