Their EDR had no record of the activity.
NorthLight
NorthLight is written by Geordie’s researchers, engineers and product specialists. Our aim is to light the way for security, IT and AI teams navigating the new waters of secure agentic operations.
You'll find real accounts of what we see in customer environments, key product releases, and practical guidance for leading and operationalizing teams.
Incident Analysis
Short accounts of what we have seen in real customer environments.
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Showing 40 of 40 posts
An AI agent was being used to mine Bitcoin.
Shadow agent activity crossed an organizational boundary
Agent Behavior Comes From More Than the Model
Model capability is one input to what an agent does. Separating three terms shows where the rest comes from: model, agent and harness.
Personal Agents Act Without Being Asked
One personal agent posted a user's private financial audit into his company's exec Slack channel. Another ran 155 traces and used more than 300,000 tokens without a single prompt. We look at what GrokBot and OpenAI's Dots did on their own, and what security teams need to see as it happens.
What happens when an agent tampers with its own record?
With agents our industry has a chain-of-custody problem. Until a record leaves the agent’s control, the custodian of the evidence is the same agent being investigated.
Personal agents, shared platforms, and unintended disclosure
Shane Mac’s account of an agent posting his bank balances into his company’s executive Slack channel is worth reading closely. It describes a failure pattern that will recur as agents share context, tools, and outputs.
How Security Teams Can Operationalize the OWASP Agentic Skills Top 10
OWASP’s new Agentic Skills Top 10 gives security teams a shared vocabulary for agent skills. Most of its checks assume you already know what’s installed. Here's our read on the four risks to prioritize which unlock all the others - and why these can only be realized through a true behavioral understanding of your agents
Shelved Agents and the Case for Agentic Lifecycle Management
When enterprises iterate on their agent workflows, older agents rarely get decommissioned.
The Hidden Cost (and Savings) of AI Agent Skills
The same agent capability can cost ~10,000 tokens as an MCP server or ~100 as a skill. What that gap costs at scale - and why it's a governance signal.
The Golden Catalog Is a Comforting Fiction
Pre-approved agent template libraries optimize for control over a static asset, when the thing they're trying to control isn't static at all.
Why (and how) we're helping customers connect agent spend to the agent work driving it
Move from uncertainty to informed confidence on both risk and spend, with cross-platform spend aggregation and token-efficiency mapping.
Refusal Training Has a Blind Spot: Skills Run Before the Model Reasons
A malicious skill called Clawsights ran a shell command before Claude Code's model ever reasoned about it. Refusal training was never in a position to stop it – here's the mechanism, and what to check instead.
Context as the Attack Surface: A New Class of Agent Compromise
A new academic paper shows AI agents can be compromised without a single malicious instruction, just by corrupting what the agent trusts. Here's how the attack works, why most current defenses miss it, and where security teams should focus instead.
After AISI, Anthropic, and OpenAI: Where Agent Governance Needs to Go Next
Four disclosures in three weeks, each one showing something new, all pointing to the same governance requirements.
MCP 2.0: How the 2026-07-28 Release Changes Agent Governance in the Enterprise
What is new and what MCP 2.0 means for security teams
Confidently Shipping Agents After OpenAI-Hugging Face
What this incident shows about governing AI agents
Where to Find Geordie at Black Hat 2026
Meet with the Geordie team at Black Hat USA 2026 in Las Vegas, August 1–6
Six Practical Security and Governance Steps for Adopting Claude Cowork
How to adopt Cowork quicker using built-in configurations alongside contextual visibility and controls
The harness: three ways to secure and govern the operational infrastructure of AI agents
Matching security and governance to teams moving fast on AI agent deployments.
Securing AI Agents Means Following Behavior, Not Boundaries
A practical view of agent security from our work on OWASP's latest guidance.
Geordie Extends Microsoft Partnership to Support Agent Control Specification
Helping customers keep agent controls consistent as their infrastructure evolves.
Geordie AI Raises $30M Series A as Enterprises Race to Govern Autonomous AI Agents
Henry Comfort, CEO and co-founder of Geordie AI, on raising a $30M Series A and what it means for enterprises racing to govern autonomous AI agents.
Prompt Injection Isn’t the Main Problem in AI Agent Security
Prompt injection gets most of the attention in AI agent security. The real risk sits in how agents behave across systems, tools, and context over time.
Where to Find Geordie AI at RSAC 2026
Meet with the Geordie AI team at RSAC 2026 in San Francisco
Claude Desktop Extensions RCE Vulnerability: A Technical Analysis of Configuration Risk and Agentic AI Governance
A remote code execution vulnerability in Claude Desktop Extensions received a CVSS 10.0 score this week. Looking at the technical requirements for exploitation reveals something more nuanced - and more instructive for agentic AI governance.
AI Agent Guardrails Explained: What They Cover and What They Don't
Guardrails are often treated as a universal safety layer for AI agents, but they're not. This piece explains what guardrails actually control, why they behave differently across coding agents, SaaS integrations, and custom-built systems, and how they differ from the runtime agentic remediations that sit alongside them.
Agentic AI Governance: Why MCP Gateways Fall Short
The autonomous nature of AI agents requires purpose-built agentic AI governance platforms. These complement MCP gateway infrastructure with behavioral observability, contextual risk assessment, and dynamic intervention capabilities.
AI Agent Risk: What Executives Need to Know to Innovate Safely and Confidently
AI Agents aren’t just software. They make decisions that can reshape your business. Left unchecked, that power creates new forms of risk. This guide helps executives cut through the hype and focus on visibility, governance, and accountability.
Governing AI Agents: Best Practices to Scale Safely
Building on research from Berkeley, this article outlines five barriers to enterprise AI adoption and the AI governance best practices to overcome them with visibility and accountability.
Building AI Agents? Here’s What Developers Want Leaders to Know
Building AI Agents promises speed and scale, but developers know the pitfalls. Explore their insights on risks, friction points, and the steps leaders can take to enable safe, resilient adoption.
Enterprise AI Governance: A Practical Roadmap to Scale AI Agents
Enterprises are experimenting with AI agents, but scaling safely requires enterprise AI governance. This guide outlines how to move AI adoption from experimentation to enterprise-wide deployment with clarity, governance, and trust.
From Connectivity to Collaboration: APIs, MCPs, and A2A in Agentic Plans
APIs, MCPs, and A2A form the foundation of AI Agent systems, yet their roles are often fragmented. This article explains how they connect, align, and collaborate, giving leaders the clarity to evaluate, govern, and scale AI adoption with confidence.
Making AI Accountable: How Explainability and Observability Meet Regulation and Trust
AI adoption is accelerating, but trust and accountability are essential. Learn how explainability, observability, and behavioral oversight help security teams meet regulations, manage risk, and scale AI Agents with confidence.
How The Next MCP Release Shapes Agentic AI in Enterprise
What the next Model Context Protocol (MCP) release means for enterprise security and risk teams running AI agents in production.
Moving Beyond Static Prompts to Living Playbooks: What ACE Means for Enterprise AI Governance
ACE moves enterprise AI governance beyond static prompts to living playbooks. This research note explains the model, why prompts don't scale, and where enterprises can apply it today.
OpenClaw and the Emergence of Personal AI Autonomy Infrastructure
Why OpenClaw matters less as a vulnerability story and more as a signal that personal autonomous agents have arrived.
Rethinking Security: The Limits of Endpoint Controls in an AI Agent World
Endpoint controls like EDR and XDR remain critical to enterprise defense, but they weren’t built for AI Agents. As these systems operate beyond traditional endpoints, leaders need new approaches to gain visibility, reduce risk, and extend security with confidence.
Seeing the Risks Clearly: How Leaders Can Evaluate AI Investments with Confidence
AI adoption is accelerating, but not every investment is safe. Risk mapping gives security and business leaders visibility to evaluate AI tools, weigh business impact against exposure, and make confident, accountable decisions that balance innovation with resilience.
The New Attack Surface: Why AI Agents Need Taint Analysis
As AI Agents evolve into autonomous actors, securing them requires techniques beyond identity and access controls. This article explores how taint analysis can extend visibility into agent workflows, uncover hidden risks, and strengthen enterprise AI security.
Unlocking Safe Innovation: Why AI Agents Must Be Transparent
AI Agents can’t be black boxes. Leaders need clear visibility into how they operate to manage risk, meet compliance, and enable safe innovation across the enterprise.
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