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Why securing AI agents requires a new approach
AI agents do not behave like traditional software. Securing them means moving beyond access and protocols to govern autonomous systems in real time.
What are AI agents?
AI agents are like digital employees, with roles, access, data, and the ability to make decisions in pursuit of goals in real time. The minimum viable definition of an agent is a large language model equipped with at least one tool. The tool can be anything from an API to an MCP to a SaaS connector.
Agents can chain tools, make instant decisions, and have the capability to impact operations at a speed and call orders-of-magnitude beyond any employee or team.
Agents are unpredictable, and their decisions do not follow a specific logic chain, meaning the requirements for controls, behavioral observability, and contextual governance are critical. Our proprietary context engine, Beam, reduces risk in real time with automated, proactive mitigations.
Even if you test agents pre-deployment, the risks and behaviors won't remain the same. Know which agents are in use, by which teams, and for what work, so you can guide adoption and governance.
What are agentic risks?
Agents amplify existing risks while also creating new blind spots.
- Identity hijacking through delegated credentials
- Prompt injection into Generative AI systems
- Data exfiltration through unsafe tool use or memory corruption
Agents can produce unsafe or incorrect outcomes even when no system is breached and no permissions change. Risk emerges when context, tools, or coordination subtly drift in ways traditional controls cannot detect.
- Inference drift from context corruption, where poisoned memory entries or contaminated retrieved context shape future decisions
- Tool impersonation within workflows, caused by look-alike tools, outdated references, or ambiguous capability descriptions
- Silent failure through partial task completion, where agents report success despite skipped steps or degraded outcome
Geordie is built to secure agents however they're built and wherever they live, whether they're:
Agents fail silently.
Agents make decisions continuously, adapt to context, and operate across systems within a fixed perimeter. Risk doesn't appear as a single event, it emerges over time.
Beam is Geordie’s risk mitigation engine that contextually guides agent decisions in real time, keeping actions aligned with enterprise policies.
Core platform capabilities
Continuous visibility across your agentic footprint
Understand what your agents are
doing in real time
Proactive risk mitigation with Beam
Geordie's architecture collects and correlates data from three critical vantage points: code, cloud, and the endpoint.
Geordie maps to international and business-specific frameworks with behavioral context and continous verification
- EU AI Act
- OSWAP Agentic Top Ten
- ISO 42001
- NIST AI RMF
- OECD AI
From Model Context Protocol (MCPs) to behavioral governance
MCP Gateways | Protocol Layer
- Protocol-layer tool mediation and traffic control
- Visibility ends at the transaction boundary
- Governs access, not agent behavior across workflows
Geordie | Behavioral Layer
- Agentic fingerprinting across code, cloud, and the endpoint
- End-to-end behavioral telemetry across workflows
- Beam for real-time contextual governance preventing unsafe actions
