How to Reduce Generative AI Security Risks and Prevent Data Loss

Generative AI (GenAI) security risks are the ways generative AI models and AI-powered applications can expose sensitive data, produce unsafe or misleading outputs, or be manipulated by attackers. Risk most often enters through prompts, AI responses, training data, retrieval data, plugins, APIs, and connections to SaaS apps, email, cloud services, and collaboration tools. 

Unlike traditional AI risks, GenAI security risks often begin with everyday user behavior. Employees might paste confidential information into a public AI tool, rely on an inaccurate answer, or use AI-generated content that violates policy. Risk is not limited to the model. It also includes the people, data, and workflows around it.

Types of Generative AI Systems 

The increasing use of GenAI is expanding the enterprise risk surface. Some of the most common GenAI tools include: 

Large language models (LLMs) that generate, summarize, translate, or analyze text based on user prompts and context.

• AI copilots that assist users inside productivity, collaboration, developer, and security tools, often with access to enterprise data.
• Chatbots and virtual assistants that answer questions, automate support, or retrieve information from connected sources.
• Code-generation tools that create, review, or transform software, which can introduce security flaws if output is not tested and reviewed.
• Image, audio, and video generators that can create synthetic content that supports business workflows but can also enable impersonation or fraud. 

GenAI systems use models trained on large datasets to generate new content. In enterprise environments, risk can enter at any stage of the AI workflow, from user prompts and enterprise data to model outputs and connected systems. Security teams must secure the full AI workflow and its attack surfaces, not just the model.

A typical GenAI interaction looks as follows:

1. Input: A user enters a prompt, uploads a file, pastes code, or asks an AI tool to analyze business data. Sensitive information can be exposed at this stage if controls are missing.

2. Processing: The model interprets the prompt during inference. Retrieval-augmented generation (RAG), embeddings, or context windows can bring additional enterprise data into the interaction.

3. Output: The AI system generates text, code, summaries, recommendations, or other content. Outputs can be inaccurate, policy-violating, maliciously influenced, or too revealing.

4. Integration: Plugins, APIs, copilots, browsers, email, cloud storage, and SaaS integrations can expand what the AI system can access or affect.

5. Logging and retention: Prompts, outputs, files, and metadata may be stored for audit, improvement, or troubleshooting. Without governance, these records can create additional data exposure.

GenAI is changing how employees work, communicate, and make decisions. However, those productivity gains also create new risks. A single unsafe prompt can expose regulated data, intellectual property (IP), customer information, or security details in seconds. 

Data loss can be expensive. According to IBM, the average cost of a data breach in 2025 was $4.44 million, while malicious insider incidents averaged $4.92 million. At the same time, organizations face growing compliance obligations and brand risk. Breaches can trigger fines, audits, and legal action, and new regulations such as the EU AI Act continue to increase scrutiny of how organizations use AI. 

Key reasons GenAI security is critical for enterprises include: 

Increased Human Risk

Many AI security incidents do not require sophisticated attacks. They happen when employees enter sensitive data into unapproved AI tools, trust inaccurate outputs, or act on AI-generated content without reviewing it. AI can make existing human risks more frequent and more costly. 

Data Exposure at Scale

Employees use AI tools to summarize contracts, analyze customer records, draft emails, troubleshoot code, and much more. Without visibility into how AI is being used, sensitive data can spread across teams, cloud applications, collaboration platforms, and third-party services.

Compliance and Governance Pressure

Privacy, industry, and contractual requirements still apply when data is shared with AI systems. That includes prompts, uploaded files, model outputs, logs, and generated content. Organizations should define which AI tools employees can use, what data they can enter, and which activities require monitoring or human review.

The top GenAI security risks combine technical weaknesses with human behavior and data exposure. Security leaders should prioritize the risks most likely to affect real enterprise workflows.  

• Data leakage and oversharing: Employees can paste source code, customer data, financial information, credentials, M&A details, or other sensitive content into AI tools. That information might be stored, processed, logged, or exposed outside approved channels.

• Prompt injection attacks: A prompt injection attack uses malicious instructions to manipulate an AI system. These instructions can be typed by a user, hidden in a document or email, embedded in a web page, or passed through a connected tool.

• Model manipulation and poisoning: Attackers might attempt to influence a model's training data, retrieval sources, or context so that outputs become biased, misleading, unsafe, or useful for data exfiltration.

• AI-generated phishing and social engineering: GenAI tools can help attackers create polished, personalized, multilingual messages at scale. GenAI can also support impersonation, business email compromise (BEC), fake support workflows, and synthetic media.

• Shadow AI and unauthorized usage: Employees might adopt consumer AI tools, browser extensions, or unapproved copilots without security oversight. Shadow AI reduces visibility into where data goes and can evade enterprise controls.

• Unsafe or inaccurate outputs: AI systems can produce confident but incorrect answers, insecure code, or policy-violating content. When users act on those outputs without review, organizations might face operational, legal, or security consequences. 

GenAI security should enable safe adoption, not slow innovation. The goal is to give employees approved ways to use AI while reducing data exposure, limiting risky behavior, and maintaining control over how AI is used. The following best practices can help. 

Implement Data Security Controls 

Use data loss prevention (DLP), data security posture management (DSPM), or a dedicated AI data security solution to protect sensitive data across approved tools and shadow AI. Look for capabilities such as data classification, redaction, encryption, and content inspection to keep sensitive information out of prompts, uploads, browser sessions, and AI-enabled workflows. 

Enforce Acceptable Use Policies 

Define which AI tools employees can use, what data they can enter, which roles can access specific features, and when human review is required. Policies should be clear, practical, and enforceable. 

Monitor Human Behavior and Risk Signals 

Monitor AI activity for risky behaviors such as sensitive data sharing, repeated policy violations, unusual access patterns, and exposure to targeted threats. Behavioral analytics can help identify high-risk users and focus coaching, investigation, and enforcement efforts. 

Discover and Classify Sensitive Data 

Scan on-premises and cloud environments to find and label sensitive data across structured, semi-structured, and unstructured data sources. Use classification to strengthen DLP policies and reduce the risk of sensitive data entering AI workflows. 

Map and Visualize Data Access 

Understand who can access sensitive data, when they access it, and how it is being used. Detect unusual activity and enforce least-privileged access and zero trust principles. 

Secure AI Integrations and APIs 

Use strong authentication, least-privilege access, secure API design, allow-listed plugins, and logging to secure AI integrations. AI tools should only have access to the systems and data required for their intended purpose. 

Educate Employees on Safe AI Use 

Provide role-based security awareness training to teach employees what they should never enter into AI tools as well as how to verify AI-generated content, spot AI-generated phishing, and report unsafe or unapproved AI use. 

Prepare for AI-Related Incidents 

Update incident response plans to address prompt injection, data leakage, exposed logs, compromised integrations, and malicious AI-generated content. Security teams should be prepared to investigate prompts, outputs, user activity, tool access, and data movement. 

Traditional security protects infrastructure, identities, endpoints, networks, applications, and data. GenAI security must protect those same assets while also managing model behavior, user prompts, AI-generated outputs, and data flowing through AI workflows.

Proofpoint helps organizations reduce GenAI data security risks by protecting the people and data at the center of AI adoption. Teams need visibility into how employees use approved AI tools and shadow AI, controls that prevent sensitive data exposure, and guidance that helps users make safer decisions. 

Data Security for AI helps teams monitor approved and shadow AI use, block or redact sensitive content in prompts and uploads, and secure data without stopping safe AI adoption. 

AI Access Security helps organizations discover AI use, reduce shadow AI risk, inspect activity, maintain audit trails, and enforce policy across AI interactions. 

Combined with DLP, DSPM, Insider Threat Management, Core Email Protection, and targeted security awareness training, Proofpoint helps organizations protect people, data, and AI-enabled workflows. 

Request a custom demo to learn more about how Proofpoint helps secure AI-driven workflows and reduce human-driven AI risk.