Generative AI Security: Risks, Controls and Best Practices

When ChatGPT went viral in late 2022, the debate inside most organizations was simple: should employees be using this at all? Three years later, that debate is over. Employees are using generative AI tools whether IT has approved them or not. The question security teams are actually dealing with now is harder: how do you secure something that moves this fast, touches this much data and doesn't fit neatly into any control you already have?

That's what generative AI security is about. Not theoretical model risks. Not abstract AI ethics. The practical problem of sensitive data flowing into tools your organization may not fully see, govern or control.

This guide covers what generative AI security means in practice, the specific risks that matter most for enterprise data programs and how to build a program that lets your teams use AI without exposing your most sensitive information.

What Is Generative AI Security?

Generative AI security refers to the set of practices, controls and technologies organizations use to protect sensitive data, intellectual property and regulatory compliance when employees interact with AI tools, specifically large language models (LLMs) and the applications built on top of them.

It's worth distinguishing this from AI safety, which concerns model alignment, harmful outputs and broader societal impact. Generative AI security focuses on a narrower operational problem: protecting your data from unauthorized exposure, leakage and misuse when AI is in the workflow.

What makes GenAI data security different from traditional data security is the nature of the risk surface. Legacy DLP programs were built to stop data from leaving through known channels: email, USB drives, web uploads. Generative AI introduces dynamics those controls weren't designed to handle.

• Employees paste documents, contracts, source code and customer records into AI tools to summarize, translate or reformat content. That data is now moving through a channel most existing controls don't inspect.
• AI-generated outputs can embed sensitive information in ways traditional content inspection struggles to detect, because the sensitivity isn't in the original file format. It's reconstructed in the output.
• AI tools are being adopted faster than IT can evaluate them. Many employees are using personal accounts on platforms their organizations have never reviewed or approved.

The result is a data security gap that is wide, fast-growing and largely invisible to organizations that haven't updated their programs to account for it.

Why Your Existing Controls Have a Blind Spot

Most data security programs were built around a clear model: data moves through defined channels, and you inspect those channels. That model breaks down with generative AI for a few reasons.

First, shadow AI follows the same pattern as shadow IT but moves faster. Employees adopt AI tools for legitimate productivity reasons, often well ahead of any organizational review. A 2025 report from Menlo Security found that 68% of employees used personal accounts to access free AI tools, with 57% entering sensitive data. That activity is invisible to tools that only monitor approved channels.

Second, enterprise AI tools like Microsoft Copilot are only as secure as the permissions of the user operating them. If a user has access to an overexposed SharePoint folder with unclassified M&A documents, Copilot can retrieve and summarize that data in response to a natural language query. No prompt injection required. No malicious intent. Just a governance gap that AI has amplified.

Third, traditional DLP was designed to inspect structured outputs: files attached to emails, data uploaded to web forms. It wasn't designed to analyze whether a block of AI-generated text contains reconstructed sensitive content. GenAI-aware controls have to treat prompts and outputs as data channels subject to the same policy logic as any other.

The Risks That Matter Most

Generative AI security isn't a single threat. It's a cluster of distinct risk categories that often intersect. Understanding them separately makes it easier to prioritize controls.

Data leakage through prompts

The most common and immediate risk. Employees paste sensitive content into AI tools to get faster answers. Customer records, source code, financial projections, legal documents. In many cases, the AI provider stores and learns from what users submit. That's an uncontrolled outbound data channel operating at scale across every department in the organization.

Shadow AI and unsanctioned tools

Shadow AI isn't a fringe behavior. It's the norm. Browser extensions that offer AI-powered writing assistance, personal accounts on public AI platforms, embedded AI features inside sanctioned SaaS tools that activated without IT review. Each of these represents a data flow that doesn't appear in your monitoring. You can't govern what you can't see.

Overpermissioned AI access

Enterprise copilots and AI agents are typically granted broad access to content repositories to function effectively. If the data those repositories contain hasn't been classified and access-scoped before the AI connects to it, the AI can surface sensitive data to anyone with the right prompt. Data security posture management (DSPM) addresses this risk upstream, before AI tools ever reach the data.

Agentic AI and autonomous workflows

Agentic AI systems take actions, not just generate text. They browse, write code, call APIs and interact with data autonomously. This changes the risk profile significantly. A traditional DLP policy designed to catch an employee uploading a file doesn't automatically catch an AI agent retrieving the same data from a knowledge base and forwarding it through an API call. The governance controls for agentic AI require a different architecture than those built for human-initiated actions.

Compliance and regulatory exposure

Generative AI doesn't create new compliance obligations so much as it creates new ways to violate existing ones. The EU AI Act began enforcing general provisions in early 2025, with broader obligations taking effect in August 2026. GDPR, HIPAA, CCPA and industry-specific data handling rules all apply to data processed through AI tools. Organizations that can't demonstrate visibility into where sensitive data travels through AI workflows have a materially harder compliance problem than they did two years ago.

Building a GenAI Security Program That Works

The core principle that separates programs that work from programs that stall is simple: inventory before controls. You cannot govern AI usage you haven't mapped, and you cannot enforce data policy on content you haven't classified. That sequence — discover, classify, govern, enforce — is the operational foundation of effective generative AI security.

Start with visibility

Map where AI tools are being used before you try to control them. This means looking beyond the approved tools list. Secure Web Gateway and CASB tools with SSL/TLS inspection can surface AI application usage across the organization, including unsanctioned tools employees are accessing through personal accounts or browser extensions. Shadow AI is the norm, not the exception, and your program should assume it before you've confirmed it.

Classify before you connect

Enterprise AI tools will inevitably have access to your data. The question is whether that data has been classified, scoped and governed before the AI can reach it. DSPM provides continuous discovery and classification across cloud, SaaS and on-premises data stores, identifying overexposed files, misconfigured permissions and sensitive content sitting in repositories that AI tools retrieve from by default. Classification done upstream is protection applied everywhere downstream.

Enforce at the point of use

GenAI-aware data loss prevention treats prompts and outputs as monitored channels, the same way it treats email or file transfers. Policies can block regulated data from entering an AI prompt, flag outputs that contain reconstructed sensitive content and log interactions for audit. The most mature programs extend the same policy framework governing email and endpoints to AI tool interactions, rather than building separate controls for each new AI application.

Apply controls that adapt to context

Static policies create two problems: they're too strict for most users and not strict enough for high-risk situations. Risk-adaptive approaches adjust enforcement based on user behavior and context. A first-time interaction with a new AI tool might trigger a warning. A pattern of repeatedly uploading sensitive documents to unsanctioned platforms warrants a harder response. Policy that scales with risk is policy that security teams can actually maintain.

Frameworks That Inform GenAI Security Programs

No single framework covers generative AI security end to end, but several provide useful structure:

NIST AI RMF (AI Risk Management Framework). Published by NIST, this framework organizes AI risk governance around four functions: Govern, Map, Measure and Manage. It's framework-agnostic by design and adaptable across industries and AI maturity levels. It's the most widely cited baseline for enterprise AI risk programs in the U.S.

OWASP Top 10 for LLM Applications. The 2025 edition catalogs the most critical failure points in LLM application security, including prompt injection (LLM01), sensitive information disclosure (LLM02) and supply chain risks (LLM03). It's practitioner-focused and worth reviewing when assessing AI application-layer controls.

EU AI Act. The regulation's general provisions took effect in February 2025, with broader obligations, including stand-alone high-risk systems, taking effect in August 2026. Organizations operating in or serving EU markets need to understand how their AI deployments are classified and what documentation and control obligations apply. Penalties for the most severe violations run up to €35 million or 7% of worldwide annual turnover.

How Data Security Technology Supports GenAI Governance

A complete generative AI security program requires the right technology architecture. The most effective AI security tools share a common structure: they reduce what AI can reach through continuous data discovery, enforce controls at the point of use through GenAI-aware DLP and govern access with telemetry security teams can act on.

Forcepoint DSPM provides the upstream discovery and classification layer. It scans cloud, SaaS and on-premises repositories to identify sensitive data, assess exposure risks and enable security teams to tighten the data sources AI tools can access before an incident occurs. AI-powered classification reduces false positives and improves the accuracy of downstream enforcement.

Forcepoint DLP extends that classification intelligence to AI tool interactions in real time. The same 1,700-plus classifiers and policy templates governing email, endpoints and cloud uploads apply to AI prompts and outputs, without requiring security teams to rebuild their policy taxonomy from scratch. Policies enforce across channels simultaneously, closing the gaps that exist when each channel is governed separately.

Forcepoint CASB addresses the SaaS and shadow AI surface. It surfaces unsanctioned AI application usage, applies granular allow-or-block policies and gives security teams the activity visibility they need to govern AI at the user, device and data level.

For organizations that need enforcement that scales with risk rather than applying uniform rules, Risk-Adaptive Protection adjusts policy response based on behavioral signals and context, making it practical to maintain aggressive coverage without creating friction for the majority of users who aren't posing elevated risk.

For a deeper look at how these controls connect, the intersection of AI security and data security is worth understanding as a unified discipline rather than two separate programs.

Generative AI Security Is a Data Security Problem

The organizations making the most progress on generative AI security aren't building a new security program from scratch. They're extending their existing data security strategy to cover a new set of channels. The same principles apply: know where your sensitive data is, control who and what can reach it and enforce policy consistently wherever it moves.

What changes with generative AI is the speed, the scale and the invisibility of the risk. Data that would have moved through a single monitored channel now moves through dozens of AI applications, many of them unsanctioned, across every device and network your employees use. The controls have to evolve to match that surface.

The good news is that organizations with mature data security programs already have the foundation. Classification intelligence, policy frameworks and enforcement infrastructure don't need to be rebuilt. They need to be extended.

If you want to see how Forcepoint's integrated platform connects discovery, classification and enforcement across every channel where generative AI creates data risk, explore how Forcepoint safely enables AI.