Least privilege
Agents receive only the data access and tool permissions required for the approved task.
AI Agent Security
Secure Every Identity, Tool and Action in Your AI Agent Ecosystem
AgenticLabs helps organisations design and assess AI agents that interact with enterprise data, tools and systems. We apply security controls across agent identity, permissions, data access, model interactions, tool execution, monitoring and human oversight.
What we cover
Security across the whole agent
AI-agent threat modelling
Agent identity and authorisation
MCP server and tool security
Prompt-injection and indirect prompt-injection defence
Data-loss prevention and sensitive-data handling
Human approval for high-impact actions
Logging, monitoring and incident response
Agent evaluation, red teaming and security testing
Secure software development and supply-chain controls
AI governance advisory aligned to recognised standards
Control principles
Five principles behind every agent we secure
Human control
High-impact, irreversible or sensitive actions require explicit approval.
Traceability
Inputs, decisions, tool calls, approvals and outcomes are logged appropriately.
Defence in depth
Identity, network, application, data and model-layer controls work together.
Continuous evaluation
Quality, security and behavioural risks are tested before and after release.
Ways to start
Start with an assessment or a pilot
AI Agent Opportunity Sprint
Prioritised use cases, feasibility and value assessment, security review, target architecture, roadmap and pilot recommendation.
Secure AI Agent Pilot
One working agent, integration, identity controls, human approvals, audit logging, testing and deployment documentation.
Delivery process
How an engagement runs
Discover
Understand the business problem, users, systems, data and risk.
Output: Use-case definition and success measures
Design
Define agent workflow, architecture, permissions and controls.
Output: Approved solution design
Build
Develop and integrate the agent in a controlled environment.
Output: Testable pilot
Validate
Evaluate quality, safety, security, performance and usability.
Output: Evidence and release decision
Operate
Deploy, monitor, improve and govern the service.
Output: Managed production outcome