The security architecture most organisations rely on was designed around a straightforward assumption that systems execute instructions, and threats arrive from outside. A firewall blocks unauthorised traffic. An automated playbook follows a fixed sequence. An access policy grants or denies based on predefined conditions. Given the same inputs, you get the same outputs. Everything is auditable, predictable, and bound.
AI agents operate on entirely different logic, and that difference is now one of the most significant unaddressed risks in enterprise security.
An AI agent is not an automation tool. It is a large language model connected to at least one external tool like an API, a SaaS connector, a code repository, a calendar integration. What makes it fundamentally different from traditional software is that it does not execute a fixed instruction. It pursues a goal. It reads context, reasons about what action to take next, executes that action, observes the result, and reassesses. Its decisions are made in real time and are non-deterministic and the same input can produce different outputs depending on context, memory state, and the condition of connected systems at that moment.
This is not a future risk. Agents are already operating inside most Enterprise organisations, often without security teams having a clear picture of where they are or what access they have been granted. Developers are using AI coding assistants connected to production repositories. Operations teams are running AI-assisted workflows that touch internal databases and external APIs. Business users are building agents through no-code interfaces like Microsoft Copilot Studio, Claude Code or Google Antigravity that connect directly to internal systems through a chat interface. Each of these is, functionally, an agent which is capable of observing state, selecting an action, and executing it across system boundaries without a human approving each step.
The Threat Has Moved Inside the Decision Layer
Traditional security threats depend on external manipulation – an attacker finding a way in, executing malicious code, or exfiltrating data through a compromised channel. Agentic threats introduce a different failure mode entirely. The system does not need to be breached in the conventional sense. It only needs to make an incorrect inference based on compromised information.
A single corrupted memory entry, a poisoned API response, or a tampered document can lead an agent to take unintended actions using valid credentials, calling approved tools, and producing clean logs throughout. The transactional record looks legitimate. The combined outcome violates policy. In connected environments where agents chain together, where one agent’s output becomes another agent’s input, these small deviations compound into larger systemic failures before any individual component triggers an alert. This is known as Semantic Drift.
This is the security risk that most existing controls were not designed to close. The controls themselves like identity validation, access management, data protection, all remain necessary and relevant. What has changed is that applying them to systems that make decisions, rather than execute instructions, requires a fundamentally different approach to monitoring and governance.
Visibility Before Governance
The main question to ask yourself is:
Do we actually know where agents are operating in our environment, what access they have, and what decisions they are making on our behalf?
For most mid-market and enterprise organisations, the honest answer is no. AI capabilities have embedded themselves into daily operations faster than governance has followed. The agent population is growing continuously, driven by individual teams solving their own problems, and it changes faster than annual security reviews can track.
Establishing visibility is the prerequisite for everything else. That means building a real-time inventory of AI tools and agents across the environment, understanding what permissions each has been granted, and mapping where those permissions intersect with sensitive data and critical systems. Without that foundation, governance frameworks and risk controls are operating blind.
From there, the work defines what acceptable operation looks like for each use case, and measures actual agent activity against those expectations continuously. This is where traditional security tooling typically falls short. Old tools cannot govern systems that choose their next action based on context. What is needed is the ability to detect when agent behaviour diverges from organisational intent and act at the speed and scale at which agents themselves operate.
What Organisations in Finance and Software Need to Address Now
For organisations in finance and software, the exposure is particularly acute. These sectors handle data that is attractive to threat actors, operate under regulatory frameworks that require demonstrable control over data handling and system behaviour, and are often at the leading edge of AI adoption within their organisations. The combination of high data sensitivity, regulatory accountability, and rapid deployment creates conditions where this can become a material risk quickly.
We need to ask ourselves whether or not our organisations have the visibility and control infrastructure to govern this effectively, or whether we are trying to retrofit security models designed for predictable systems to systems that make decisions.
This is an area where Nayaka works closely with Geordie AI, whose platform is specifically built to address the agentic security problem, providing the visibility, behavioural monitoring, and real-time agentic workflow auto-remediation that conventional security tools were not designed to deliver. For organisations beginning to engage seriously with agentic AI risk, understanding what is already operating in the environment and what access it has is the right place to start.
