Rethinking AI Security: Why CASB and DLP Need an Interaction-Aware Layer
Employees across organizations are using AI to automate, coordinate, and optimize complex workflows. However, this increased use of AI has also introduced new security risks that traditional security playbooks may not be equipped to handle.
The familiar security playbook to minimize AI risk is to discover the AI tools in use, gate access with CASB, add DLP rules to the mix, and track their usage. While this is not a bad security model, it has its limitations when it comes to AI.
Why CASB and DLP Fall Short for AI
Unlike conventional SaaS risk, which can be confined to an app, a file, or a structured data field, AI risk is a different animal. It rears its head in a prompt, shows up in the response generated by the AI model, and in a scenario that organizations have come to dread, it comes up in action that autonomous agents take because of a malicious prompt.
Unfortunately, none of these scenarios always cleanly align with what CASB and DLP were built to inspect. The question that CASB asks is whether a user is allowed to access a particular application, including an AI tool, and, in many cases, it governs what they can do inside. But when it comes to AI, it is also important to consider the meaning behind the conversation.
Traditional CASB controls may not evaluate the semantic and cumulative context of an AI conversation with sufficient depth. This is the gap that must be addressed. Substance matters in AI. A prompt might look harmless at first glance, but dig deeper, and its wording, context, and purpose can make it risky.
Security Must Inspect the Interaction
Controls should operate where exposure happens, and in this case, it is happening inside the exchange between a person and the AI model. That means looking closely at what’s being asked, what the model generates in response, what tools the agent invokes, what data it retrieves or transmits, and whether its resulting actions are permitted.
As opposed to just deciding whether to allow or block access to the AI service itself. A few scenarios show why that distinction matters: Using AI to come up with a blog outline for publication is a routine task. However, using it to develop go-to-market copy referencing an unannounced product puts sensitive information at risk.
- A software developer using an AI tool to answer a generic question is at low risk.
- But the same is not true for a request that includes proprietary logic.
- It can become worse if a question is linked to a real customer problem.
Whether this information is shared or generated as output, this doesn’t fall within the purview of safe AI use. When it comes to agentic workflows, the agent retrieving the approved knowledge base is not risky, but forwarding restricted internal documentation externally is definitely risky.
Extending Governance, Not Blocking Access
A default-deny doesn’t hold up in the long term. Blocking access outright looks safe on paper, but employees still have deadlines, so usage shifts to personal accounts and unmanaged extensions that CASB was never configured to see.
This scenario sees the deeper penetration of shadow AI. Reducing AI risk isn’t a battle between CASB, DLP, and interaction-level inspection. All three are important and solve different layers of the same problem.
Putting your best foot forward for managing AI risk means: Treating prompt injection and agent misuse as real, everyday risks now, rather than edge cases to be addressed later.
- Leveraging CASB and DLP to discover SaaS and AI apps in use, govern access, detect known sensitive patterns, and support compliance reporting.
- Adding an interaction layer that drills down into the semantics of a prompt, the sensitive nature of the response, and whether an agent’s action is authorized.
- Not seeing the goal as locking AI down until it’s safe by omission, but to give employees room to adopt and experiment with AI while sensitive data and agent behavior stay inside clear boundaries.
Can this person open the tool? is the wrong question for AI. The right one is whether a particular prompt is safe, whether the response is safe, and whether this action is authorized.
Build your strategy around answering these questions to ensure employees use AI productively while keeping sensitive data, IP, and agent behavior within the boundaries set for safe AI use.
Source: SecurityWeek