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Behavioral Understanding & Risk Intelligence

See what your agents are
doing and the risk it creates

Geordie observes an agent's context, its configuration and every tool call it makes – not just what crosses a gateway. That is what turns a signal into something worth acting on.

Trusted by forward thinking teams

  • Xapo Bank
  • Fitch Group
  • Interra Health
  • Synthesia
  • AlphaSense
  • A+E Global Media
  • Forge Holidays
  • OakNorth
  • 118 118 Money
  • Advt Group
  • Owkin

“We've been rolling out agentic AI across the business for a while now, and the governance question kept coming up: what's actually running, what can it do, and what happens if something goes wrong? Geordie is helping us solve these problems. It's given us real peace of mind, and they're improving their product every week. Geordie should be on every company's shortlist for agentic AI discovery and governance.”

Michael Cena
Michael Cena
Head of Cybersecurity @ A+E Global Media

Where we watch

Inside the agent's full execution loop

By the time a request crosses a boundary, the decision has already been made. Geordie is present for the decision.

  1. Context

    What the agent was told and remembers

  2. Configuration

    Instructions, skills and permissions in force

  3. Plan

    The steps it intends to take

  4. Tool call

    The action, with its arguments

  5. Response

    What came back and what it changed

A gateway sees
tool call · response
Geordie sees
the whole loop, in order, with the reasoning attached

A single, organized view of risk

Geordie analyzes agents using proprietary risk and threat modeling, incorporating external vulnerability intelligence to give you an accurate view of both operational and compliance risk.

Findings are automatically mapped to international security and AI standards such as OWASP, NIST, ISO42001, the EU AI Act, and AIUC-1, so you always know where your agentic operations stand against the policies and regulatory expectations you are accountable to.

Total
142
Scenarios
Critical
9
Scenarios
Medium
27
Scenarios
Low
36
Scenarios
Info
22
Scenarios
  • Access & Privileges

    Agents accessing systems or permissions beyond what's needed. Protects from privilege escalation, unauthorized access, and compliance violations.

    8 scenarios
  • Code Execution

    Agents running unprotected or unscoped code. Protects from malware, backdoors, and agents being weaponized to run attacker scripts.

    4 scenarios
  • Configuration

    Unsafe agent configurations and exposed services. Protects from shadow IT, bypassed security controls, and persistent attack surfaces.

    9 scenarios
  • Dangerous Operations

    Destructive commands and irreversible actions. Protects from data loss, service outages, and offensive operations.

    5 scenarios
  • Data Protection

    Agents processing sensitive data or surfacing information. Protects from regulatory penalties, IP theft, and data breaches.

    29 scenarios
  • Financial Risk

    Agents with transaction or spending authority. Protects from unauthorized transactions, fraud, and uncontrolled spending.

    7 scenarios

The audit challenge

Native agent logs are not an audit trail

Sessions can be rewritten or deleted, and the log lives inside the system it is meant to hold to account. Security teams need a record that survives the thing it describes.

Requirement Native session logs Geordie
Cannot be altered after the fact Sessions can be edited or cleared Immutable once written
Held outside the agent Stored by the harness it records Independent of the agent and its user
Complete across the estate One format per platform, stitched by hand One schema across every harness
Covers reasoning, not just calls Varies; often output only Prompt, plan, response and invocation
Ready for an examiner Retention at the vendor's discretion Exportable to your SIEM and your auditors

See what an agent did, in order

Tool calls, data reaches and escalations as they happen, with full history. When a review starts with "what actually happened", the answer is one page away.

  • Behavior baselines per agent, with drift flagged
  • Prompt source and identity on every action
  • Alerts into Slack, Teams or your SIEM
See a sample timeline

claims-triage · session 8f2c

19 events
  1. 14:02:11 Prompt Review claim 44921 and settle if within tolerance
  2. 14:02:12 Plan Read claim · check policy limits · adjust payment · note the file
  3. 14:02:14 Tool · claims.read In baseline · 6,200 prior invocations
  4. 14:02:16 Tool · payments.adjust First occurrence for this agent · plan step cited an unreviewed skill
  5. 14:02:19 Response Settled at £4,120 · case note written

Baselining & anomaly detection

Normal has to be learned before abnormal means anything

Geordie builds each agent's own behavioral baseline, then measures every session against it.

Tool use, last 14 sessions

Fourteen sessions of settled behavior Session 14 · 2.4× baseline
  • Per agent, not per fleet

    A research agent's normal is not a payments agent's normal.

  • Behavioral, not signature-based

    New attack patterns do not need a known signature to look wrong.

  • Continuous, not scheduled

    The baseline moves as legitimate behavior evolves.

Actionable by construction

Behavioral understanding is what makes a signal worth acting on

Geordie does not just find agent risk. It gives teams enough to close the loop.

A flag without context

Unusual tool call detected

agent-4471 · 14:02:16

  • No owner, so it lands in a queue
  • No baseline, so nobody knows if it is unusual
  • No cause, so there is nothing to fix

A finding you can act on

claims-triage invoked payments.adjust for the first time

Owner: Claims ops · cause: unreviewed skill added on 3 Sept · 6,200 prior sessions never did this

  • Named owner, so it goes to a person not a backlog
  • Baseline attached, so severity is arguable with evidence
  • Cause identified, so the fix is specific – withdraw the skill or bind the tool

Remediation is where this leads – Beam applies the control inside the same execution loop the finding came from.

Explore Beam

In practice

What behavioral understanding catches

A skill that looks legitimate and is not

The agent's plan starts citing an instruction nobody reviewed, and its tool use widens the same day.

Drift from the configured brief

An agent scoped to read starts writing, one step at a time, over several weeks.

Sensitive data on an unexpected path

Customer records read by an agent whose brief never mentioned them, then summarized outward.

A loop that never resolves

The same plan step repeating for hours, consuming tokens and producing nothing.

Sub-agents nobody accounted for

One workflow fanning out into a dozen delegated agents, each with its own reach.

Prompt injection that succeeded

Instructions arriving from content the agent read, then showing up in its plan.

The visibility Geordie gives me helps me articulate the return of control, the reduction of risk, and how well we're managing AI adoption.
Jon Mattey CISO, Forge Holiday Group

See what your agents are actually doing

Connect one platform and we will show you a real session end to end – prompt, plan, tool calls, response – with the baseline it sits against.

  • 30 minutes, run by an engineer
  • Read-only access, revoked whenever you like
  • A findings summary you can forward internally
Where is your company located?

Questions, answered

Still unsure? Ask us anything – we answer in a day.

Both, merged into one classification. Geordie combines what an agent is configured to do with what it is observed doing, which produces a dynamic risk picture rather than a static configuration snapshot. Classification goes deep – to the individual tool, user, credential and permission – and wide, across shadow areas, unsanctioned agents and tool types gateways never see.
No. The immutable log is a tamper-proof record held by Geordie, and it supports compliance reporting, incident response and forensic investigation without the cost of pushing full agent telemetry into a SIEM. It can still feed downstream into your SIEM and out to your auditors when you want it there.
Models are used for analysis: behavioral understanding, risk scoring, anomaly detection and policy recommendation – the places they're best placed. Policy creation and enforcement are deterministic, with no LLM-as-judge and no LLM-generated policies, because enterprises governing autonomous systems need enforcement that behaves identically every time. Probability is reserved for understanding risk, never for deciding what happens next.
Agent operations are continuously aligned against both your internal AI policies and external regulatory frameworks, including the NIST AI RMF and the EU AI Act. Because that mapping runs against the behavioral record continuously rather than on a review cycle, audit readiness is always-on instead of reconstructed manually ahead of each assessment.
An endpoint sensor sees interaction events on a device, but not the agent's own configuration or its behavior over time, and that ceiling doesn't extend to cloud-hosted, code-level or multi-agent environments. Network-only tools see traffic but not the agent. Agents are systems spanning surfaces, and each of those approaches covers one.
Yes. Risk thresholds and controls are configurable by department, role and use case, so the same agent platform can carry a different risk profile and different controls in engineering than in marketing. That matches an organization's real risk tolerance rather than applying one blanket policy to a vendor.