How to Secure AI: 7 Tips to Protect Data in Any AI Tool

AI is already in your environment. The question is whether your security controls are keeping up with it.

Most organizations discovered this gap the same way: an employee pastes a client contract into ChatGPT to summarize it, a developer uses a public model to debug code that includes API credentials, or a finance analyst uploads a financial model for a board summary. None of it looks like an incident in the moment. All of it creates real exposure.

Securing AI is not a single problem. It is seven distinct problems, each requiring its own set of controls. This post walks through each use case, what the risk looks like in practice and what effective protection actually requires.

Why Securing AI Demands a Different Framework

Traditional security was designed around predictable data movement: files moving through known channels, users operating within established workflows, systems staying in assigned places. AI breaks all three of those assumptions at once.

Data now flows into and out of AI systems through channels that existing controls were never designed to inspect. Employees interact with models through browser sessions that look like ordinary web traffic. Autonomous agents access, reshape and redistribute sensitive data faster than any human-generated action could. AI security extends data protection discipline into this new interaction layer, covering prompts, outputs, retrieval pipelines and agent behavior alongside the traditional channels organizations already govern.

According to Gartner, 69% of organizations suspect their employees are using prohibited generative AI tools, and 33% of employees have admitted to entering sensitive information into unapproved tools. That data does not disappear once it enters a prompt.

Securing AI is, at its core, a data security problem. The controls that protect sensitive data across endpoints, cloud and SaaS are the same controls that need to extend to AI, not replace them.

The seven use cases below represent the most common and consequential AI security problems organizations face today. They are also the areas where the gap between risk and control is widest.

1. Monitor What AI Your Employees Are Actually Using

Before you can govern AI usage, you need to know what AI is being used. That sounds straightforward. It is not.

Most organizations significantly underestimate the number of AI tools their employees access. Browser extensions, embedded AI features inside sanctioned SaaS platforms, personal accounts for ChatGPT or Gemini, AI-powered code assistants learning from private repositories: these all create data egress paths that neither IT nor security teams have visibility into by default.

Effective AI usage monitoring means decrypting and inspecting web traffic at scale, categorizing AI destinations dynamically and correlating access patterns to data sensitivity. Secure Web Gateway plays a central role here, identifying AI-bound sessions that appear as ordinary encrypted HTTPS traffic. The goal at this stage is visibility, not restriction: understand which tools are in use, which users and groups are accessing them and what data they are bringing in before deciding what to govern and how.

Reporting matters equally. Usage data only drives decisions when it surfaces in dashboards that compliance, security and leadership teams can act on. Audit-ready logs of AI interactions are increasingly a regulatory requirement, not just a best practice.

2. Identify and Block Unauthorized AI Apps

Sanctioned tools are not automatically governed tools. And ungoverned AI creates blind spots that persist until a breach or a compliance audit surfaces them.

Shadow AI follows the same pattern as shadow IT but moves significantly faster. A 2025 report from Menlo Security found that 68% of employees used personal accounts to access free AI tools like ChatGPT, with 57% of them entering sensitive data. Employees are not waiting for IT approval. They find tools that help them work and use them, whether or not those tools have been evaluated for data handling practices.

Blocking unauthorized AI applications requires controls that operate at the application layer, not just the network perimeter. Cloud Access Security Broker provides the app-level enforcement needed to distinguish between approved and unapproved AI tools, apply access policies by user group and data sensitivity, and guide employees toward sanctioned alternatives rather than simply blocking access and generating friction. The practical outcome is an AI tool inventory that security teams can actually govern.

3. Classify Sensitive Data Before AI Can Reach It

Data classification is the upstream control that makes every other AI security measure more precise. Without it, enforcement decisions are guesses.

When a copilot or AI agent can access a SharePoint folder containing unclassified merger and acquisition documents, no amount of prompt filtering prevents that data from flowing into generated outputs. The exposure happens before the AI interaction begins, at the point where sensitive data is overexposed, mislabeled or sitting in repositories that AI tools can retrieve from by default.

DSPM for AI addresses this by discovering and classifying sensitive data across cloud, SaaS and on-premises sources before AI tools can reach it. That means identifying overshared files, misconfigured permissions and dormant data with high sensitivity, then providing the classification foundation that downstream enforcement tools need to apply smarter, more accurate policies. Microsoft Information Protection tagging and auto-labeling capabilities extend this classification intelligence into Microsoft 365 environments, ensuring Copilot and other productivity tools interact with data that is correctly labeled before outputs are generated.

4. Extend Data Loss Prevention to AI Tools

The data loss prevention controls organizations have built for endpoints, email and cloud applications do not automatically extend to AI tools. That gap is where the most common AI-related data exposures happen.

Employees paste regulated data into AI prompts, upload files to AI assistants and use AI-generated outputs in ways that move sensitive content into channels with no policy coverage. DLP for AI closes that gap by treating AI prompts and outputs as data channels requiring the same governance as email or file transfers. The same classifiers and policy logic that govern traditional channels extend to AI tool interactions, including AI features embedded in sanctioned enterprise platforms.

The practical enforcement model works at the data level, not the application level. Rather than blocking AI tools outright, which employees typically circumvent, DLP identifies and blocks specific sensitive data types within prompts: PII, source code, regulated health information, financial records. An employee using an AI code assistant for legitimate debugging continues working. The same employee attempting to paste a customer contract into a public model encounters a targeted block on the sensitive content, not a blanket restriction on the tool.

5. Stop Data Leaks at the Prompt Level in Real Time

Prompt security operates at the moment of interaction: inspecting what goes into an AI system and what comes back out, in real time, before sensitive content can move.

The risk runs in both directions. Sensitive data entering a prompt can be retained by external AI vendors, used to train future models or simply logged in ways the enterprise has no control over. Sensitive data appearing in AI outputs can be redistributed by employees who have no way of knowing the content they received should not have been surfaced. Both directions require inline inspection.

Inline DLP operating at the browser and web layer applies policy decisions at the speed of AI interactions. When an employee submits a prompt containing personally identifiable information to an AI tool, the control fires before the data leaves the enterprise network, not after. Output inspection applies the same logic in reverse: flagging generated content that contains regulated data and logging the interaction for audit purposes. This is particularly relevant for ChatGPT Enterprise, Claude Enterprise and Microsoft Copilot, where organizations have API-level access to apply controls at the conversation layer. Forcepoint recently announced an integration with the Claude Compliance API, extending DLP and DSPM workflows directly to Claude Enterprise.

6. Secure Microsoft Copilot and Prevent Oversharing

Microsoft Copilot respects the permissions of the user operating it. That is precisely what makes it a significant data security risk for organizations that have not audited and tightened their underlying access controls.

When employees have accumulated permissions to sensitive repositories far beyond their current job requirements, Copilot can surface that data in generated outputs, responses and summaries. Employees do not need to know those files exist. The AI finds them. A misconfigured SharePoint folder, an unreviewed OneDrive permission or a missing sensitivity label can translate directly into an AI-generated response that contains content the recipient was never supposed to see.

Securing Copilot requires controls at two layers. The first is data posture: DSPM continuously maps which data Copilot can reach, identifies overshared or mislabeled content and ensures that the data AI assistants index is classified and scoped correctly. The second is real-time enforcement: DLP and CASB apply inline controls to Copilot interactions through the Microsoft API, inspecting prompts, monitoring outputs and automatically correcting Microsoft data classification tags when they are inconsistent. For a detailed breakdown of how these risks play out, the post on Microsoft Copilot data risks covers the five most common exposure scenarios.

7. Secure Autonomous AI Agents Before They Act on Your Behalf

Every use case above involves an AI system that responds to a human prompt. Agentic AI changes that equation entirely.

Autonomous AI agents do not wait for instructions on each step. They plan, decide and act across business environments: searching knowledge bases, retrieving files, calling external APIs, drafting content, sending messages and triggering downstream workflows. An agent with broad access permissions and no runtime visibility is a significant blast radius waiting for a triggering condition. As agentic AI moves from pilots into production, the security controls needed to protect it are still catching up.

Securing autonomous agents starts with the data they can reach. Classify what agents have access to before deployments go live. Apply least-privilege access so agents operate only within the scope their use case requires. That scope narrows the blast radius if something goes wrong, whether through misconfiguration, a compromised dependency or a prompt injection attack that redirects agent behavior entirely.

Prompt injection is the most acute threat in agentic environments. When an agent retrieves content from an external source, a document, a webpage or a connected system, malicious instructions embedded in that content can redirect the agent's actions without the user ever knowing. Forcepoint X-Labs has identified and documented prompt injection payloads in the wild, confirming these attacks are actively weaponized across the open web, not merely theoretical.

Data-layer controls address what agents can access and move. But agentic security also requires coverage at the runtime layer, where agents execute. Runtime controls enforce policy on live API traffic, detect anomalous agent behavior and ensure that what an agent does at execution time matches what it was authorized to do. Forcepoint's partnership with F5 addresses this directly, combining Forcepoint's data security capabilities with F5's AI runtime protection to secure both the data agents touch and the infrastructure through which they act.

Organizations running insider risk programs are increasingly extending that same logic to AI agent behavior. The threat profile is similar: an entity with privileged access that can take actions outside its intended scope. Securing agents means monitoring their behavior continuously, logging what they retrieve and from where, and treating any deviation from expected patterns as an incident worth investigating.

AI Security Is Not Simply a Deployment Checklist

The seven use cases above are not sequential. They operate in parallel, across different parts of your environment, driven by different teams with different priorities. The organizations that manage AI security effectively treat it as a continuous program: classifying data before AI can reach it, enforcing policy at the interaction layer, monitoring usage and behavior continuously and tightening controls as adoption expands.

The practical starting point for most organizations is visibility. You cannot govern what you cannot see. Knowing which AI tools employees use, what data flows into and out of them and where your most sensitive data sits relative to what AI can access gives security and compliance teams the foundation to build effective controls without restricting the productivity AI is supposed to deliver.

For a broader look at the tools and controls that support each layer of this program, the post on AI security solutions walks through the solution categories, their best-fit use cases and how to evaluate them against your current environment.