AI & Agentic Security

Secure AI systems, agents and MCP-style integrations before autonomy turns assumptions into actions.

THE BUYER'S QUESTION / EMERGING TECHNOLOGIES

Who or what can an autonomous workflow act on behalf of?

Examine tool access, instructions, identity, data exposure and control boundaries in AI-enabled workflows.

START WITH THE PRIORITY

Tell us the objective, your environment and the decisions you need to make. We will help define a focused engagement.

BEFORE YOU COMMITWant to know what working with Akita looks like?Explore the engagement approach →

AKITA / AGENCY UNDER CONTROL

What you get

AI and agent threat modelling

Tool, connector and MCP-style trust-boundary review

Agent identity and permission design

Prompt, data and action-path abuse testing

Human-approval, logging and containment controls

How the engagement works

Identify what the model or agent can see, decide, call and change.

Test misuse paths across prompts, tools, identities, data and connected systems.

Design least-agency permissions, approval points, telemetry and containment.

FAQ

Do you test LLM prompt attacks?

Yes, where relevant, but the scope also covers the surrounding tools, identities, data and action paths.

What is different about agent security?

The system can take actions, so permissions, tool boundaries and oversight matter as much as model behaviour.

Can this support secure MCP use?

Yes. Connector trust, tool exposure, identity, secrets and allowed actions can all be assessed.

Validation in practice

Explore how attack-path testing can focus remediation on the risks that matter.

READY TO DEFINE THE WORK?

Already have an AI security requirement or draft scope? Send it to Akita for a focused scoping conversation.