Gen AI security

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4 chapters

Gen Ai security

Practices and controls that protect sensitive enterprise data when employees use generative AI tools and assistants — including LLMs, AI writing tools, code assistants, and multimodal AI platforms

Chapter 1

What is GenAI Security?

Your employees are already using AI tools. The question isn't whether generative AI has entered your enterprise — it has. The question is whether you have any visibility or control over what data is going into it.

The Problem

The alternative isn't less control. It's smarter control. AI guardrails let security and IT operations teams define exactly what employees can and cannot do with AI tools — and enforce those boundaries automatically, in real time.

Prompts are not ephemeral. File uploads don't disappear.

Sensitive information entered into an AI tool — a client contract, a patient record, a financial model, proprietary source code — can be retained by the AI vendor, used to improve their model, or exposed in a third-party breach.

And in most enterprises today, that data is leaving through the browser with no security control in the way.

Chapter 2

Gen ai Security Closes the Gap

Definition

GenAI security is the practice of identifying, monitoring, and controlling how enterprise data flows into and out of generative AI tools. It encompasses the full lifecycle of AI interaction: what employees type into prompts, what files they upload, what outputs they bring back into business workflows, and which AI tools they are accessing in the first place.Traditional data loss prevention (DLP), cloud access security brokers (CASBs), and endpoint detection tools were designed for a world where data leaves through files, emails, and network transfers — not through natural-language conversations with an AI assistant running in a browser tab.

What Gen AI Security Covers

AI tool interactions happen inside the Document Object Model (DOM) of a web page — the live, rendered environment inside the browser. When an employee types a prompt into ChatGPT or uploads a file to an AI assistant, that action occurs entirely within the browser.

Visibility

Visibility into which AI tools employees are accessing, and with what credentials

Monitoring

Visibility into which AI tools employees are accessing, and with what credentials

Policy Enforcement

Policy enforcement that governs if data can be shared with which AI tools

Compliance Alignment

Compliance alignment for regulated data categories (PII, PHI, CUI, intellectual property, financial data)

Governance and Audit Capabilities

Governance and audit capabilities that demonstrate AI acceptable use to regulators, insurers, and boards

Chapter 3

GenAI Risks & Data Leakage

Generative AI tools introduce a new class of enterprise data risk because sensitive information shared in a prompt or file upload may be retained by the AI provider, used to train future models, or exposed in a breach.

5 Categories of Risk

Data Retention and Model Training Risk

Risk 1

Many consumer and freemium AI tools retain conversation history by default and may use user inputs to improve their underlying models. Enterprise data entered into a personal AI account exists outside the organization's data governance controls — and in many cases, there is no reliable mechanism to delete it.

Compliance and Regulatory Exposure

Risk 2

Sharing protected health information (PHI) with an AI tool that is not a HIPAA-covered entity creates an immediate compliance violation — regardless of whether a breach occurs. The same logic applies to PII under GDPR and CCPA, CUI under CMMC 2.0, and financial data subject to SOX or PCI DSS.

Shadow AI and Identity Risk

Risk 3

Employees frequently access AI tools using their corporate email address but outside of single sign-on (SSO) — creating accounts that bypass identity governance controls entirely. Without SSO, there is no MFA enforcement, no lifecycle management, and no visibility into what data those accounts have touched.

Insider Risk and Unintentional Exfiltration

Risk 4

The majority of AI-related data exposure events are not malicious — they are the result of employees using AI tools the way they were designed to be used, without understanding the data implications. A sales rep pasting a customer contract into an AI summarizer. A developer using an AI code assistant with proprietary algorithms.

Prompt Injection and AI Manipulation

Risk 5

Malicious actors can embed adversarial instructions inside documents, web pages, or data that employees feed into AI tools — causing the AI to behave in unintended ways, exfiltrate data, or generate outputs that serve the attacker's objectives.

How Data Leakage Happens in the Browser

GenAI data leakage most commonly occurs directly in the browser — the point where employees interact with AI tools in real time. Whether an employee pastes confidential text into a prompt, uploads a client file to an AI summarizer, or uses an AI coding assistant with proprietary code, that data leaves the enterprise environment through a browser interface that traditional security tools were never designed to monitor.

Why the browser is the critical blind spot:

Network-layer tools like SASE analyze traffic — but they cannot inspect what a user types into a web-based AI prompt field. EDR tools monitor processes and file system activity — but a prompt typed into a browser tab is neither a process nor a file.

Scenario	Data at Risk	Why Traditional Tools Miss It
Employee pastes Q3 revenue projections into ChatGPT to draft a board summary	Financial / Confidential	No file transfer occurs; no network policy triggers
HR uploads candidate PII to an AI resume screening tool	PII — potentially GDPR/CCPA-regulated	Tool is outside SSO; CASB has no visibility into the session
Developer pastes proprietary source code into an AI code completion assistant	Intellectual property / Source code	Browser text input is not a file — no DLP rule fires

Chapter 4

The 5 Stage Security Framework

Securing AI prompts, inputs, and file uploads requires controls that operate at the point of interaction — in the browser, in real time, before data leaves the enterprise.

5 implementation principles

Enforce at the right layer

principle 1

Guardrails that block entire AI tool domains create maximum friction for minimum control. Browser-based guardrails enforce at the content level: the tool is accessible, low-sensitivity work proceeds normally, and only genuinely risky interactions are intercepted.

Start with Warnings Before Blocking

Example 2

Begin with warning-and-log mode across all AI tool interactions, then progressively tighten to block mode for the highest-risk data classes. This gives employees time to understand policy boundaries and gives security teams usage data to calibrate policies accurately.

Make Messages Helpful, Not Punitive

Example 3

"This action was blocked" creates frustration. "This content contains customer PII that cannot be shared with this tool — try removing the customer identifiers and using the data in aggregate form" is a guardrail that employees can work with.

Align Guardrails to Policy, Not IT Instinct

Example 4

“This action was blocked" creates frustration. "This content contains customer PII that cannot be shared with this tool — try removing the customer identifiers and using the data in aggregate form" is a guardrail that employees can work with.

Visibility Informs Better Policy Over Time

Example 5

When you can see which tools employees are using, which data types they're attempting to share, and where the policy is firing most frequently, you can refine your guardrails to be more precisely targeted — reducing friction for low-risk use cases while tightening controls where real risk concentrates.

The Neon Cyber Approach