Best tools to secure GenAI usage (ChatGPT/Copilot/Gemini) and prevent sensitive data in prompts in real time

AI moves fast. Your data can’t be allowed to move uncontrolled—especially when employees are pasting sensitive content into ChatGPT, Copilot, or Gemini in real time.

The core problem isn’t that GenAI is unsafe by default. It’s that most organizations still rely on static controls and siloed tools that were never designed to see what users are typing into AI prompts, or to adapt when risk changes second by second. You don’t just need visibility into GenAI usage; you need the ability to stop data loss as it happens—without shutting down innovation.

This guide ranks the best tools and approaches to secure GenAI usage and prevent sensitive data from leaving your environment through prompts, with a focus on real-time controls, unified policies, and AI-native classification.

Quick Answer: The best overall choice for securing GenAI usage in real time is Forcepoint Data Security Cloud. If your priority is deep Microsoft 365/Copilot integration with strong but narrower coverage, native Microsoft Purview DLP is often a stronger fit. For organizations that want extensibility through browser/inline proxies and API-based controls, cloud access security broker (CASB)/SSE platforms with GenAI controls can be effective in targeted use cases.

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At-a-Glance Comparison

Rank	Option	Best For	Primary Strength	Watch Out For
1	Forcepoint Data Security Cloud	Enterprises that need unified, real-time GenAI data protection across web, email, endpoint, cloud apps, and network	AI-native classification and Risk-Adaptive Protection with a single policy enforced everywhere	Requires strategic rollout to fully replace fragmented DSPM/DLP toolsets
2	Microsoft Purview DLP for Copilot & M365	Organizations heavily standardized on Microsoft 365 and Copilot	Deep integration with M365 workloads and Copilot policies	Limited reach beyond Microsoft ecosystem; may miss non-Microsoft GenAI and shadow SaaS
3	CASB/SSE platforms with GenAI controls	Teams focused on securing GenAI primarily via web/SaaS traffic and browser proxy	Good for controlling access and basic content inspection for popular GenAI tools	Often report-heavy and channel-specific; classification depth and cross-channel consistency can be limited

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Comparison Criteria

We evaluated each option against the realities of GenAI usage in large enterprises:

• Real-time GenAI prompt protection:
  Does the tool see what users are sending to ChatGPT, Copilot, and Gemini in real time—and can it block, redact, or coach the user before sensitive data leaves?

• AI-native data classification and accuracy:
  Can it accurately distinguish business-critical IP, regulated data, and trivial data using AI (beyond simple regex), and do those classifications persist across channels and data stores?

• Unified policy and coverage breadth:
  Can you create a single policy and enforce it consistently across AI tools, cloud apps, web, email, endpoints, and network—without managing separate rule sets and consoles?

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Detailed Breakdown

1. Forcepoint Data Security Cloud (Best overall for unified, real-time GenAI protection)

Forcepoint Data Security Cloud ranks as the top choice because it brings Self-Aware Data Security to GenAI: it discovers and classifies sensitive data, understands how people actually use that data, and enforces one policy in real time across AI tools, cloud apps, web, email, endpoints, and network.

What it does well:

• AI-native, explainable classification (AI Mesh Data Classification):
  Forcepoint uses a Small Language Model (SLM) and other AI classifiers—not a black-box LLM—to tag data with hyper-accurate, explainable labels. That means you can:

  • Distinguish between real IP (e.g., proprietary formulas, algorithms, or design docs) and generic content.
  • Apply nearly 2,000 policy templates and classifiers to regulated data (PCI, PHI, PII, financial, etc.).
  • Extend the same classification across unstructured data (files, emails, documents) and structured data (databases like Microsoft SQL, Oracle, MySQL; data lakes like Snowflake and Databricks).
    The output isn’t just a report—it’s persistent tagging that drives enforcement everywhere GenAI might see that data.

• Real-time GenAI prompt control with Risk-Adaptive Protection:
  Forcepoint doesn’t just detect that someone used ChatGPT. It looks at what they’re trying to send:

  • Intercept prompts at the endpoint and web layer before they reach ChatGPT, Copilot, or Gemini.
  • Analyze content for classified data (e.g., contracts, customer records, source code) and behavioral risk signals.
  • Apply Risk-Adaptive Protection (RAP) to dynamically choose the right action:
    • Allow and silently log
    • Coach the user in-line (“This prompt includes confidential client data—please revise.”)
    • Redact sensitive segments before sending
    • Block the prompt outright for critical violations
      This keeps employees productive with GenAI while preventing them from pasting sensitive content into prompts.

• Single-policy framework: create once, enforce everywhere:
  Too many DSPM and DLP deployments stop at visibility. Forcepoint is built to turn visibility into control:

  • One policy framework for AI tools, SaaS, web, email, endpoint, and network.
  • Consistent rules for “No customer PII to public GenAI tools,” “No source code to external AI,” or “Restrict confidential documents to approved copilots” applied across channels.
  • Centralized dashboards for executives and auditors, showing where regulated data is exposed, how GenAI is being used, and what incidents RAP has prevented.

• Continuous discovery, prioritization, and remediation:
  Securing GenAI isn’t just about what users type today—it’s about the data estate GenAI can reach tomorrow. Forcepoint:

  • Continuously discovers shadow data and dark data across cloud repositories and endpoints.
  • Highlights over-permissioned files and mislocated sensitive data that GenAI tools or copilots could access.
  • Automates remediation: adjust permissions, move data to secure repositories, quarantine or delete ROT and mislocated files, and deduplicate.
  • Feeds this posture back into GenAI enforcement decisions, so controls stay aligned with the actual risk surface.

Tradeoffs & Limitations:

• Platform consolidation and rollout effort:
  Forcepoint is designed to replace fragmented DSPM, DLP, and point GenAI controls with a single platform. The upside is unified control and lower long-term overhead; the tradeoff is that realizing full value typically involves:
  • Rationalizing existing DLP/DSPM tools.
  • Phasing in single-policy enforcement across multiple channels.
  • Aligning stakeholders (security, data owners, compliance, line-of-business) around new workflows.

Decision Trigger:
Choose Forcepoint Data Security Cloud if you want real-time GenAI prompt protection tied to AI-native classification, and you prioritize a single-policy framework that secures AI tools, cloud apps, web, email, endpoint, and network together rather than as separate projects.

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2. Microsoft Purview DLP for Copilot & M365 (Best for Microsoft-centric environments)

Microsoft Purview DLP is the strongest fit when your world revolves around Microsoft 365 and Copilot and you want to start with integrated controls inside that ecosystem.

What it does well:

• Tight integration with M365 workloads and Copilot:
  Purview DLP works natively with Exchange Online, SharePoint Online, OneDrive, Teams, and now Copilot scenarios. For organizations operating primarily in Microsoft 365, this means:

  • Policies that restrict what Copilot can access and surface based on M365 sensitivity labels.
  • Ability to set rules for specific Copilot experiences (e.g., preventing exposure of “Highly Confidential” documents in user prompts or responses).
  • Familiar admin experience for teams already managing Defender and Purview.

• Built-in sensitivity labels and compliance templates:
  If you’ve already labeled content in Microsoft 365, Purview DLP can apply those labels when controlling Copilot. This can be effective for:

  • Blocking or auditing specific label combinations in Copilot requests and responses.
  • Using Microsoft’s prebuilt templates for common regulations within the M365 data estate.

Tradeoffs & Limitations:

• Limited reach beyond the Microsoft estate:
  The strength of Purview is also its constraint:

  • Protections are most effective when data lives in Microsoft 365 and when GenAI usage is primarily Copilot.
  • If your users are also in ChatGPT, Gemini, or other SaaS tools in browsers, coverage typically becomes patchy unless you add other products (browser proxies, web gateways, CASB).
  • Shadow data outside M365 (other SaaS, unmanaged repositories, endpoints, data lakes) may not be consistently classified or protected.

• Policy fragmentation across channels:
  Many organizations end up with one set of rules in Purview for M365/Copilot, and separate DLP/CASB configurations for web, email, endpoint, and non-Microsoft SaaS. This can cause:

  • Inconsistent outcomes (a prompt blocked in Copilot but allowed in ChatGPT via browser).
  • Higher operational overhead to keep policies in sync.

Decision Trigger:
Choose Microsoft Purview DLP if your immediate priority is to control Copilot and M365-based GenAI usage, and you’re comfortable using additional tools to cover non-Microsoft AI tools, cloud apps, and hybrid data estates.

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3. CASB/SSE platforms with GenAI controls (Best for targeted web/SaaS enforcement)

Cloud Access Security Broker (CASB) and broader Secure Service Edge (SSE) platforms that offer GenAI controls stand out when your goal is to put a guardrail primarily on web-based AI tools like ChatGPT and Gemini, especially via browser or inline proxies.

What they do well:

• SaaS and web visibility for GenAI usage:
  CASB/SSE tools are effective for:

  • Discovering which GenAI apps (ChatGPT, Gemini, etc.) users are accessing over the web.
  • Controlling access by group, device, or location (e.g., blocking public GenAI entirely for certain departments).
  • Applying basic content inspection on traffic to/from these tools—often enough to flag obvious PII or simple patterns.

• Browser and inline proxy enforcement:
  With browser plugins or inline gateways, some solutions can:

  • Monitor what users paste into web prompts.
  • Block uploads or restrict downloads from GenAI tools.
  • Apply coaching messages or step-up enforcement for certain violations.

Tradeoffs & Limitations:

• Classification depth and context awareness:
  CASB/SSE tools generally weren’t built as AI-native classification engines. Common drawbacks:

  • Reliance on pattern matching and basic detectors rather than SLM-based, explainable classification.
  • Difficulty distinguishing high-value IP from low-risk content, especially across unstructured documents.
  • Limited ability to persist classifications back into data stores and drive remediation.

• Channel-specific, report-heavy approach:
  Many CASB deployments end up as another silo:

  • Strong for web/SaaS traffic monitoring, but disconnected from endpoint DLP, email security, and DSPM.
  • Useful for visibility—“who is using which GenAI tools”—but weaker at orchestrating true discovery → classification → remediation → enforcement loop across the data estate.

Decision Trigger:
Choose CASB/SSE with GenAI controls if you want to quickly get a handle on GenAI traffic over the web, and you’re willing to accept channel-specific controls that may need to be complemented with dedicated DSPM/DLP and endpoint capabilities for full coverage.

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Final Verdict

Securing GenAI usage across ChatGPT, Copilot, Gemini, and the next generation of AI tools requires more than point controls. It requires an operating model where:

• Sensitive data is continuously discovered—including dark data and shadow data in cloud repositories, databases, and endpoints.
• That data is classified with AI Mesh-level accuracy using explainable SLM models that work across structured and unstructured environments.
• Risks are prioritized and remediated—permissions repaired, mislocated data moved, ROT and duplicates cleaned up—before GenAI tools ever touch them.
• Controls are enforced in real time via a single-policy framework that covers AI tools, cloud apps, web, email, endpoint, and network, adapting to behavior and context with Risk-Adaptive Protection.

Among the options, Forcepoint Data Security Cloud is built for this continuous loop. It unifies DSPM, DLP, AI-native classification, and adaptive enforcement into one platform, so you can let your people use GenAI to move fast—without letting your data move uncontrolled.

If you’re Microsoft-centric and need to protect Copilot inside M365 first, Purview DLP is a pragmatic starting point, but you’ll likely still need additional tools for non-Microsoft GenAI and broader data estates. CASB/SSE platforms can add valuable visibility and control at the web layer, yet they’re best seen as part of a broader Self-Aware Data Security strategy, not the strategy itself.

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