Employees across organizations are using AI to automate, coordinate, and optimize complex workflows. Some of that usage flows through tools that IT has reviewed and approved. But a significant portion happens through personal accounts, browser extensions, and shadow AI services that security teams have never seen, let alone sanctioned. The result is a growing gap between the controlled enterprise environment and the uncontrolled ways employees are actually working with AI.
The traditional security playbook for minimizing AI risk focuses on discovery. Security teams try to inventory every AI tool in use, then gate access with a Cloud Access Security Broker (CASB), apply Data Loss Prevention (DLP) rules, and continuously monitor usage. This model has worked for years to secure software-as-a-service sprawl, but it has clear limitations when applied to AI. The reason is simple: AI risk does not look like traditional application risk, and it cannot be contained with the same controls.
Why CASB and DLP Fall Short for AI
Conventional SaaS risk can usually be confined to an application, a file, or a structured data field. AI risk is different. It emerges in a prompt written by a user. It appears in a response generated by a model. And, in the scenario that organizations now dread most, it materializes in an action taken by an autonomous agent because of a malicious or manipulated prompt. None of these scenarios cleanly align with what CASB and DLP were designed to inspect.
A CASB asks whether a user is authorized to access a particular application and, in many cases, governs what the user can do inside that application. That question is still important, but it is not sufficient for AI. When a user interacts with an AI tool, the meaning behind the conversation matters. The same prompt that looks harmless in isolation can become risky when combined with previous context. The model might infer confidential details from indirect references, or it might be steered by an embedded instruction that a DLP rule would never catch. Traditional CASB controls do not evaluate the semantic and cumulative context of an AI conversation with enough depth.
This is the gap that must be addressed. Substance matters in AI. A prompt may appear innocent at first glance, but its wording, context, and purpose can make it dangerous. Exposure will not always come from a user pasting an account number or API key directly into a conversation. A user might unknowingly share sensitive details across multiple prompts. Those details can be described indirectly, but they are still enough for the model to understand and potentially act on them.
Consider information shared around a vendor contract that is about to close, or a summary of an outage incident report. In both cases, there is real business risk associated with the information being shared, even if the content does not match a DLP rule. A CASB cannot see this risk because the user is allowed to access the AI app. The risk appears later, inside the prompt itself. Security teams are then caught in a bind. If they squeeze CASB policies too tightly, users shift to unmanaged apps that no one can see. If DLP is too permissive, sensitive data inevitably leaks.
Security Must Inspect the Interaction
Controls should operate where exposure happens. For AI, that means looking closely at what is being asked, what the model generates in response, what tools the agent invokes, what data it retrieves or transmits, and whether the resulting actions are permitted. The focus must move beyond simply deciding whether to allow or block access to an AI service. The focus must move inside the exchange between a person and the model.
A few scenarios illustrate why this distinction matters. Using AI to develop a blog outline for publication is routine and low risk. But using it to create go-to-market copy that references an unannounced product puts sensitive information at risk. A software developer asking an AI tool a generic programming question is also low risk. But a request that includes proprietary logic, especially when linked to a real customer problem, is not safe.
Agentic workflows introduce even more complexity. An agent that retrieves information from an approved knowledge base is not inherently risky. But that same agent forwarding restricted internal documentation externally is clearly dangerous. Summarizing a publicly available document is low risk. Reconstructing confidential information from a partial context is not. Treating retrieved content as passive data is expected behavior, but when instructions are embedded cleverly enough that the agent ingests and follows them, prompt injection becomes a serious risk because the model cannot reliably distinguish data from instructions. Authenticating an agent or session is a baseline control, but it is not enough when a low-risk request can suddenly turn into a high-risk action. Security needs AI anomaly detection to decide what should be allowed in real time.
Extending Governance, Not Blocking Access
A default-deny posture does not hold up over time. Blocking AI access outright looks safe on paper, but employees still face deadlines and pressure to deliver. When official tools are locked down, usage shifts to personal accounts and unmanaged extensions that CASB was never configured to see. This is how shadow AI becomes deeply embedded in an organization, often with far less security oversight than the tools that were blocked.
Reducing AI risk is not a battle between CASB, DLP, and interaction-level inspection. All three are important, and they solve different layers of the same problem. CASB and DLP provide foundational visibility and control. They help discover SaaS and AI applications, govern access, catch known sensitive patterns, and support compliance reporting. What they cannot do is understand the meaning of a conversation, the sensitivity of a generated response, or the authorization status of an agent action. That requires an additional layer.
The shift in mindset is essential. Security teams should not try to lock AI down until it is safe by omission. They should give employees room to adopt and experiment with AI while keeping sensitive data and agent behavior inside clear boundaries. This means threat actors targeting AI through prompt injection and agent misuse must be treated as real, everyday risks now, not edge cases to be addressed later. It also means leveraging CASB and DLP for their strengths while adding an interaction layer that drills into the semantics of a prompt, the sensitive nature of the response, and whether an agent action is authorized.
The old question, “Can this person open the tool?” is the wrong question for AI. The right question is whether a particular prompt is safe, whether the response is safe, and whether the action is authorized. Building a security strategy around those questions allows employees to use AI productively while keeping sensitive data, intellectual property, and agent behavior within the boundaries set for safe use.
Source: SecurityWeek News