Companies are moving beyond using artificial intelligence simply to generate content or answer questions. AI systems are increasingly being connected to enterprise identities, internal data, APIs, databases, and other applications. In this new paradigm, AI agents are being given the ability to retrieve information, make decisions, call tools, and take actions.
This shift presents a fresh set of challenges for cybersecurity teams, as the depth of AI’s integration into enterprise systems may render traditional security measures insufficient. As per Hakimuddin Wadlawala, Founder of Aquila I, companies must understand how their AI systems behave, what they can access, and what actions they are taking.
This transition is particularly pivotal as businesses move towards more autonomous, agentic AI systems that can interact with enterprise applications and data with limited human intervention. Traditional cybersecurity systems focus on identifying known vulnerabilities and clear malicious activities. However, AI systems bring an added layer of complexity, as their behavior can change based on the data, instructions, and systems they interact with.
An AI agent, for example, might use valid credentials to access a database, which, when viewed in isolation, may not be suspicious. But if the same identity suddenly gains additional privileges, accesses an unfamiliar resource, and makes an unusual sequence of tool calls, the combination could indicate potential security issues.
The complexity of the security challenge is further amplified with the growing use of agentic AI, capable of performing sequences of tasks, interacting with tools and applications, and making decisions along the way. An agent could access a data source it has never used before, make an unusual series of tool calls, communicate differently with another agent, or move beyond its normal scope. While none of these actions may independently indicate an attack, the pattern could signal that something has gone wrong.
Therefore, security teams must have visibility across the AI environment, including AI systems, agents, model APIs, RAG pipelines, MCP integrations, identities, tokens, infrastructure, and connected data systems. This comes at a time when concerns about AI systems’ growing capabilities are becoming more prominent among technology leaders.
Microsoft co-founder Bill Gates recently warned of the risks associated with advanced AI systems and has highlighted concerns about AI-enabled cyberattacks. The developments around Anthropic’s Claude Mythos underscore why cybersecurity has become such a crucial part of the conversation around advanced AI systems. Anthropic, through their Project Glasswing, used Claude Mythos Preview with around 50 partners to identify more than 10,000 high- or critical-severity vulnerabilities across important software.
However, the same capabilities that benefit cybersecurity researchers can be misused, potentially creating risks if powerful AI systems fall into the wrong hands, thus widening the scope of cybersecurity challenges. Securing AI means looking at more than just the model.
It is pivotal to understand the relationships between the AI system, its identity, the tools it can utilize, the data it can access, and the surrounding infrastructure. Companies are moving towards the concept of AI Systems Detection & Response (AISDR), which aims to increase visibility into AI systems and the interactions between models, agents, identities, infrastructure, tools, and data, shifting the focus from whether an action is permitted to whether it is contextually sensible.
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