What is GenAI Security?

GenAI security refers to protecting enterprise environments from the emerging risks of generative AI tools like ChatGPT, Gemini, and Claude. As these tools gain adoption, they introduce data leakage, compliance, and shadow AI risks. This article defines GenAI security and outlines enterprise strategies to ensure safe and responsible AI use.

GenAI Explained

GenAI security is the practice of identifying and mitigating risks introduced by generative AI tools such as ChatGPT, Copilot, and Claude within enterprise workflows. These tools enhance efficiency and innovation but also introduce a new and rapidly evolving AI attack surface that traditional cybersecurity solutions often fail to cover. GenAI security addresses this gap by managing sensitive data exposure, enforcing organization-wide AI usage policies, and detecting unsafe, non-compliant, or malicious AI behavior. It combines technical safeguards like data loss prevention (DLP), browser-based monitoring, and access controls with robust AI governance frameworks aligned with company policies and regulatory standards. Unlike AI development security, which focuses on securing model training and infrastructure, GenAI security protects the usage layer, where employees interact with external AI tools, ensuring safe, policy-aligned, and responsible enterprise AI protection.

Primary Risks of GenAI in the Enterprise

As organizations accelerate the adoption of generative AI tools, they must also address a new category of threats. These risks emerge not from malicious actors alone, but from the way generative AI interacts with data, users, and external environments. Below are the most pressing AI vulnerabilities and security risks enterprises need to manage.

1. Intellectual Property and Confidential Data Exposure

One of the most immediate and critical GenAI risks is AI data leakage. Employees often paste confidential information like customer PII, source code, business plans, or financial projections into GenAI tools like ChatGPT without realizing the implications. These prompts may be stored, processed, or used for further training, creating a permanent loss of control over that data. Even when vendors claim not to train on input data, the data may still be cached or logged in session history leaving the door open for breaches or misuse.

Example: A finance team member uses ChatGPT to generate an executive summary and pastes a spreadsheet of Q4 revenue data into the prompt. That financial information could now be stored by the model provider or potentially exposed in future queries by other users.

2. Regulatory and Compliance Violations

Unmonitored GenAI usage can easily result in violations of data protection regulations like GDPR, HIPAA, PCI-DSS, or CCPA. These laws require strict handling of personal, health, or payment data, something most third-party AI tools are not contractually or architecturally prepared to ensure.

Example: A healthcare provider uses an AI writing assistant to draft a patient care summary, including medical history. Even a single prompt containing PHI (Protected Health Information) shared with an external AI tool could be a reportable HIPAA violation, risking regulatory fines and reputational damage.  In highly regulated sectors, just one such incident can invite sustained scrutiny from regulators and auditors.

Enterprises must treat AI prompts like outbound communications and apply the same AI policy and data governance rigor to stay compliant.

3. Shadow AI Usage

Employees often use personal accounts or unauthorized AI tools without IT knowledge creating shadow AI environments. While Shadow AI is often well-intentioned and has become deeply embedded in workflows to enhance productivity, they end up falling outside security policies and lack monitoring or logging, making them fertile ground for compliance violations and AI data leaks and a blind spot for security and data protection teams.

Example: A sales team starts using a consumer version of ChatGPT to draft client proposals. Over time, they begin inputting pricing strategies, contract terms, and internal performance metrics, none of which are protected by enterprise DLP tools.

4. Risky Third-Party Plugins and Extensions

AI-powered browser extensions and plugins introduce serious AI vulnerabilities due to over-permissive designs. Many have access to all browsing activity, clipboard data, or session cookies to function, making them attractive targets for exploitation. 

Risks include:

• AI Injection Attacks: Malicious websites or scripts manipulate plugin prompts to extract or leak data.
• Session Hijacking: Plugins with access to session tokens may be exploited to impersonate users.
• Silent Data Harvesting: Extensions may read or transmit data without user awareness.

Most plugins are created by third parties and may not undergo the same security scrutiny as internal tools. Unvetted plugin use can result in uncontrolled data exfiltration and expose regulated information to unknown actors, representing a major generative AI data risk for the enterprise.

Example: An AI summarizer extension installed by a user has permissions to read every tab. An attacker exploits a flaw in the plugin to extract sensitive CRM data viewed by the user without ever triggering a traditional DLP or antivirus alert.

5. Erosion of Internal Security Posture

Unmonitored AI use weakens overall enterprise security posture. When employees use public AI tools through unmanaged browsers or personal accounts, sensitive data bypasses traditional security controls like firewalls, endpoint protection, or cloud DLP. Security teams lose visibility into how and where data is being handled. Over time, this erodes the organization’s ability to detect breaches, maintain audit readiness, and enforce security policies, leaving the business vulnerable to both internal and external threats. These security blind spots give attackers or careless insiders a path to exploit data without triggering standard defenses—making generative AI security an urgent priority.

Example:

Employees using GenAI tools like ChatGPT on personal devices share customer data that never touches corporate infrastructure, making it invisible to IT and compliance teams.

6. Operational and Legal Disruption

Data exposure through GenAI tools can trigger legal proceedings, audits, and internal investigations, diverting resources and disrupting day-to-day operations by delaying projects, and creating internal friction between teams seeking accountability and mitigation. Beyond financial losses from the broken deal, the organization may also face legal claims, penalty clauses, or arbitration proceedings. 

Example:

A manufacturing company discovers sensitive supplier terms were input into ChatGPT and possibly leaked. Procurement teams are forced to renegotiate contracts, while legal manages vendor inquiries and liability assessments.

These risks highlight why traditional security controls are no longer enough in the age of generative AI. From AI data leaks and shadow AI to regulatory violations and plugin-based threats, organizations must rethink how they monitor, govern, and secure AI usage across the enterprise. To dive deeper into these evolving threats and how to address them, read the full article on Generative AI Risks.

What’s Driving the Expansion of AI Attack Surface in Enterprises

The rapid rise of generative AI has fundamentally reshaped the enterprise threat landscape. What was once a clearly defined perimeter is now fractured by a growing constellation of AI-powered tools, plugins, and cloud-based workflows. These technologies boost productivity—but they also dramatically expand the AI attack surface, introducing novel security blind spots that traditional defenses were never designed to handle.

Explosion of AI Tools and AI Integrated SaaS Apps

GenAI does not equal ChatGPT. In fact, a lot has changed since ChatGPT was released in November 2022. Since then, the GenAI ecosystem has been evolving at an unprecedented pace. New models and AI-powered tools are emerging on a weekly and monthly basis, each offering more capabilities and advancements than the last. Innovation is accelerating so quickly that, according to Gartner, it’s significantly surpassing the pace of any other technology. 

Enterprises are integrating generative AI at every layer of the stack. From AI copilots embedded in developer environments to automated assistants in CRM platforms, the average employee may now interact with multiple AI systems daily. SaaS providers from Notion and Slack to Salesforce and Microsoft 365 have all launched AI-integrated features designed to enhance workflow efficiency. For users, AI-driven enhancements are becoming a standard expectation rather than a convenient add-on. GenAI has turned into an integral part of the workplace. But these same integrations often come with broad access to internal data, documents, calendars, and conversations.

This proliferation of SaaS AI tools means organizations must now secure a diverse set of external platforms that ingest sensitive information often without consistent logging, access control, or visibility. Every new integration creates a potential vector for AI data exposure, especially when default settings prioritize usability over security.

Browsers Are the New AI Workspaces

Unlike traditional enterprise applications that operate as dedicated desktop applications, most GenAI interactions take place through web browsers. Most AI tools like ChatGPT, Claude, and Gemini are accessed via the browser. While convenient, this browser-based model introduces unique browser AI risks such as Man-in-the-middle (MITM) attacks, token theft, or even browser extension exploitation become feasible if the session is not properly isolated.

Traditional security tools, which were designed for legacy enterprise applications and controlled environments, are ill-equipped to inspect or control AI interactions on dynamic browser sessions. They cannot distinguish between safe and unsafe inputs, personal vs. corporate account usage, or detect sensitive data being copied and pasted into LLM prompts. For instance, users can easily paste sensitive financial company data into ChatGPT or upload proprietary source code without triggering security alerts. This lack of real-time, context-aware visibility and control at the browser level creates significant risks, forcing enterprises to rethink their security strategies in an AI-first workplace.

AI-Powered Productivity Extensions

Browser extensions powered by generative AI, such as AI summarizers, writing assistants, or meeting note-takers, often request excessive permissions. These include access to page content, cookies, and sometimes keystrokes. Many are created by third-party developers with limited or no security oversight.

These extensions open the door to AI injection attacks, silent data scraping, or session hijacking, especially when installed on unmanaged endpoints. Once installed, they operate silently, interacting with user data in real time and transmitting it to external APIs often beyond the reach of traditional security tools.

API-Connected Workflows in the Cloud

In cloud-native environments, AI capabilities are increasingly embedded into automated workflows via APIs. Developers may wire LLMs into CI/CD pipelines, customer service flows, or data processing pipelines, often passing structured or unstructured data to third-party AI models for summarization, translation, or classification.

This creates a largely invisible AI attack surface, where sensitive data flows to and from AI services without being properly scanned or filtered. API endpoints can also be exploited to inject adversarial inputs, exfiltrate internal data, or execute AI security exploits if not properly validated.

The Observability Challenge

A major challenge in securing this new AI-driven landscape is the lack of real-time observability. Traditional security tools do not natively detect AI prompts, track AI tool usage, or identify the context of data flows within browser sessions or API interactions. As a result, organizations are blind to how, where, and when data enters or exits the AI layer. 

To protect against modern AI security risks, organizations need visibility into every interaction between users and AI—whether it’s happening in a browser tab, a SaaS integration, or a cloud API call. Without continuous monitoring, governance, and enforcement, the AI layer becomes an unmonitored gateway for sensitive data to leak, shift, or be exploited

Browser-Based DLP and Insecure Plugin Design in GenAI Ecosystems

As enterprise adoption of generative AI accelerates, the browser has become a central access point where employees interact with tools like ChatGPT, Microsoft Copilot, and hundreds of AI-powered extensions. But with this shift comes a pressing need to rethink traditional data loss prevention (DLP). Browser DLP is emerging as a vital security layer for monitoring and controlling AI usage in environments increasingly reliant on Chrome extensions, SaaS apps, and web-integrated plugins.

Why Browser-Level DLP Matters in the GenAI Era

Unlike traditional applications, GenAI tools are largely web-based and often accessed outside of sanctioned platforms. Employees frequently use browser extensions or web apps to generate code, content, or insights. This usage bypasses legacy DLP tools that focus on endpoints, email, or network traffic creating blind spots in AI data protection.

Browser-based DLP solutions address these gaps by inspecting user interactions within the browser in real-time. This allows organizations to detect when sensitive data such as source code, client records, or financial documents is copied, typed, or uploaded into AI prompts. Combined with policy enforcement, this enables organizations to block, redact, or alert on risky behavior before data is exposed.

The Hidden Risk of Insecure AI Plugins and Extensions

AI Browser extensions that enable or enhance AI functionality are especially problematic. Many are designed with broad permissions to access clipboard data, manipulate page content, or intercept inputs. Without proper vetting, these extensions introduce plugin-based data leakage and other high-severity risks, such as:

• Session hijacking – Malicious plugins may harvest authentication cookies, granting attackers access to SaaS apps or internal systems.
• AI injection attacks – Extensions can modify prompt inputs or responses, injecting malicious commands or altering output in ways that go unnoticed.
• Silent data exfiltration – Some plugins log user interactions or prompt content and send it to third-party servers without the user’s knowledge.

The risk is not hypothetical. In 2023, a popular ChatGPT extension with over 10,000 installs was found stealing Facebook session tokens, demonstrating how GenAI extension risks can escalate into full-blown security incidents.

Inter-Plugin Data Leakage

AI browser plugins often require broad permissions to access page content, input fields, clipboards, or background processes. When multiple extensions run in the same browser, these permissions can overlap, creating unintended pathways for data exposure.

For instance, a writing assistant may process document inputs while a separate plugin accesses the same DOM or local storage. Without strict data isolation, sensitive content can unintentionally flow between plugins even when neither is malicious. 

This risk grows with background processes and shared APIs, where one plugin could act as a bridge to siphon data from another. Therefore, coexisting GenAI extensions blur data boundaries, making plugin isolation and browser-based DLP essential.

Limitations of Browser App Stores

Chrome and Edge extension stores prioritize consumer access, not enterprise security. They lack deep permission audits, secure development standards, and post-publish monitoring. This allows malicious or over-permissive GenAI plugins to stay live until flagged by users or researchers. Many are built by unknown developers with opaque data practices, yet gain access to critical workflows. Browser app stores aren’t a trusted gatekeeper. Enterprises must pre-vet, control, and monitor AI plugins themselves.

Apply Zero Trust Principles to AI Extensions

Applying a Zero Trust mindset to browser extensions is essential, especially in environments with heavy GenAI use. Just as enterprises scrutinize apps, users, and devices, plugins must be treated as untrusted by default.

This means:

• Validating publisher authenticity before installation
• Auditing permission scopes to avoid overreach (e.g., clipboard, DOM, background access)
• Monitoring plugin behavior continuously, even after approval

In GenAI workflows, where plugins often access sensitive text inputs, this approach helps prevent silent data exfiltration and privilege abuse. No plugin should be trusted implicitly by enterprises. Instead, they must treat each one as a potential risk and enforce least-privilege, identity-verified access. This layered security approach ensures enterprises can embrace the productivity gains of GenAI without opening the door to plugin-based compromise or unauthorized data transfer.

Why AI Governance Is Central to Security

As generative AI tools become embedded in daily business workflows, the challenge for security leaders is no longer whether to allow AI, but how to control it responsibly. This is where AI governance becomes central to enterprise security and provides the framework to ensure secure AI usage, balancing innovation with risk management, and enabling productivity without compromising data integrity, compliance, or trust.

At its core, AI governance aligns security, legal, and compliance teams around a shared AI policy that provides a strategic and operational framework needed to control how AI tools are accessed, used, and monitored, ensuring enterprise readiness as AI adoption scales. The framework must include: 

1. Policy Creation for AI Usage

Effective AI governance starts with a clear AI usage policy that defines which tools are approved, what data can be used, and where AI is appropriate or restricted. It eliminates ambiguity, aligns stakeholders, and sets the foundation for secure, compliant AI adoption across teams.

2. Role-Based Access to AI Tools

Role-based access controls (RBAC) ensure employees only use AI tools appropriate to their roles, enabling productivity while protecting sensitive data. It relies on the principle that not all employees need or should have access to the same AI capabilities or datasets for their scope of work. Developers, marketers, and legal teams, et, each get tailored access, reducing risk and preventing misuse. These controls prevent accidental misuse while supporting legitimate productivity needs based on business function and risk profile.

3. Usage Approvals and Exception Handling

AI governance frameworks should also include workflows for managing exceptions and special use cases. If an employee or team needs access to a restricted AI tool or use case:

• They should submit a formal request.
• The request should go through a risk review process involving security or compliance stakeholders.
• Temporary access can be granted under specific guardrails, such as additional monitoring or manual output review.

This system of usage approvals and exception handling ensures flexibility without sacrificing oversight.

4. Centralized Logging and Review of AI Interactions

Governance is not only about defining what is allowed but also ensuring visibility into what is actually happening. Centralized logging of AI tool interactions provides the auditability required for both internal accountability and external compliance.

This includes recording prompt and response history, capturing metadata like user ID, session time, and browser context, etc. These records help detect misuses, investigate incidents, and refine policy over time.

5. Monitoring for Policy Violations or Anomalous Behavior

To close the loop between policy and protection, AI governance must be paired with real-time monitoring. Security teams need systems that can:

• Detect prompts containing restricted data (e.g., keywords, regex patterns).
• Flag or block unauthorized AI tool usage in the browser or on unmanaged devices.
• Identify anomalous behavior, such as excessive prompt frequency, unusual access times, or unexpected plugin activity.

By continuously monitoring for policy violations, governance transforms from a static document into an active, adaptive security layer.

Adapting Governance to a Rapidly Evolving AI Landscape

Existing governance frameworks like ISO/IEC 42001 (AI Management Systems) and NIST’s AI Risk Management Framework provide useful starting points, but they must be adapted to account for the unique pace and behavior of GenAI tools. These tools don’t operate like traditional software; they evolve in real time, process unpredictable inputs, and are often consumed via consumer-grade interfaces.

Therefore, AI governance must be iterative and dynamic. It should be reviewed frequently, reflect real-world usage patterns, and evolve alongside AI capabilities and threat intelligence. 

Governance: The Bridge Between Enablement and Protection

In summary, AI governance is the connective tissue between responsible AI enablement and enterprise-grade protection. It ensures that AI tools are not just allowed but are used safely, ethically, and in full compliance with internal and external mandates. Without a formal governance structure, enterprises face a fragmented environment where employees freely experiment with ChatGPT, Copilot, and other tools—often pasting sensitive data into public models or using unvetted plugins. This opens the door to compliance violations, data leaks, and unmonitored AI decision-making that could impact operations or legal standing. Therefore, as GenAI continues to evolve, governance must remain flexible, enforceable, and deeply integrated into the organization’s broader security architecture.

Best Practices for GenAI Security

• Map All AI Usage in the Organization

The first step in managing GenAI risk is mapping out how it’s being used across the company. As part of this mapping process, organizations must monitor:

• Which GenAI tools are in use? Are they accessed via web apps, browser extensions, or standalone software?
• Who is using them? Are they in R&D, marketing, finance, or other departments?
• What are they using GenAI for? Tasks like code reviews, data analysis, and content generation?
• What kind of data is being entered into these tools?  Are employees exposing code, sensitive business data, or PII?

Once you have answers to these questions, you can start building a clear usage profile, spot high-risk areas, and create a plan that allows for productivity while ensuring data protection.

• Implement Role-Based Access and Prevent Personal Accounts

Apply role-based access controls to limit exposure based on job function and data sensitivity risk. Developers may need access to AI code assistants, while legal or finance teams may require restrictions due to sensitive data handling. Use approval workflows for exceptions, allowing flexibility under governance oversight. 

To keep sensitive information out of unsecured LLM tenants, organizations should block personal logins and mandate access through corporate accounts that come with security features such as private tenants, zero‑training commitments, strict data‑retention controls, and stronger privacy safeguards.

• Deploy Browser-Level AI DLP

Generative AI tools are predominantly accessed through the browser, making AI DLP at the browser level a critical control point. Browser-based data loss prevention tools can:

• Detect when sensitive data is being entered into AI prompts
• Block or redact regulated information in real-time
• Provide log interactions for compliance and audit readiness

Browser-based DLP controls are essential for monitoring AI usage that bypasses traditional endpoint or network security tools.

• Monitor and Control AI Extensions

AI-powered browser extensions introduce risk via over-permissive access to web pages, keystrokes, and session data. Apply AI extension control policies that:

• Restrict installation of unapproved or unknown plugins
• Audit extensions in use and assess their permissions
• Block extensions with excessive access to enterprise applications

Review plugin behavior continuously to detect anomalous activity or silent data capture.

• Educate Employees on Secure AI Use

Security awareness programs in enterprises must include trainings for secure GenAI use as well. Organizations must train employees to:

• Recognize what data should never be shared with AI tools.
• Use approved platforms and follow policy guidelines.
• Report suspected misuse or unauthorized tools.

Make AI security part of regular training cycles to reinforce responsible behavior as AI tools evolve.

Real-World Impacts of Poor GenAI Security

While GenAI tools like ChatGPT can accelerate productivity, their misuse or unsecured deployment has already led to significant breaches, compliance violations, and reputational damage. Weak AI governance, over-permissive extensions, and unsanctioned tool use have proven to be major contributors to real-world security failures, emphasizing why GenAI risk management is no longer optional.

1. Source Code Exposure at Samsung

In early 2023, Samsung made headlines after engineers pasted proprietary source code into ChatGPT to debug errors. While the intent was to improve productivity, the impact was immediate: highly confidential code was potentially exposed to OpenAI’s models and storage systems. This incident triggered an internal ban on ChatGPT and prompted a company-wide audit of AI tool usage.

Takeaway: Even a well-intentioned use of GenAI can lead to irreversible data loss if proper usage boundaries aren’t defined and enforced.

2. Misuse of ChatGPT Leads to Compliance Investigation at DWS Group

DWS Group, an asset management subsidiary of Deutsche Bank, was investigated after employees used ChatGPT for investment research and client communication. Regulators flagged this as a compliance failure, noting that financial institutions must vet AI tools and ensure outputs meet regulatory accuracy and data handling standards.

Impact: Regulatory scrutiny, reputational risk, compliance policy tightening.

3. Teleperformance – Data Privacy Concerns over AI Monitoring Tools

Teleperformance, a global customer service provider, faced scrutiny over using AI-driven surveillance tools to monitor at-home employees. The tools were found to capture personal and sensitive data, including video footage, without proper user consent or safeguards. Data protection regulators raised AI misuse and ethical concerns.

Impact: Public backlash, data protection audits, and operational changes in AI tool deployment.

4. AI Hallucination Leads to Legal Risk

An international consulting firm faced reputational fallout when a generative AI tool used for internal research returned inaccurate information in a client-facing deliverable. The hallucinated content, presented as factual, led to a damaged client relationship and contract loss.

Takeaway: Generative AI impact extends beyond security as tools that generate flawed or misleading outputs can trigger reputational, operational, and legal damage if used without proper review.

5. Increased IT Workload from Shadow AI Tool Sprawl

In the absence of centralized controls, employees often adopt unauthorized AI tools and plugins to boost productivity. This sprawl burdens IT teams with tracking, evaluating, and mitigating unknown risks.

Example: A Fortune 500 company discovered over 40 unapproved AI tools actively used across departments, each with different access levels and unclear data handling practices.

Impact: Increased IT overhead, fragmented risk landscape, urgent need for governance.

6. Security Incidents via Malicious Extensions or Plugins

GenAI browser extensions can introduce AI injection, silent data access, or session hijacking risks, especially when overly permissive or not vetted by security teams.

Example: A ChatGPT extension on the Chrome Web Store was found stealing Facebook session cookies, granting attackers full account access.

Impact: Account takeovers, browser-level breaches, erosion of user trust.

Without strong GenAI security and governance, enterprises risk more than just technical vulnerabilities. They face legal, reputational, and operational consequences. Proactively addressing these risks with usage-layer controls, DLP, and role-based governance is essential to enable safe and productive AI adoption.

How LayerX Secures GenAI Use

As enterprises embrace GenAI tools, the challenge of protecting sensitive data from unintended exposure becomes urgent. Traditional security tools were not built for the dynamic, browser-based nature of GenAI interactions. This is where LayerX steps in—delivering purpose-built, browser-native defenses that provide real-time visibility, control, and protection against inadvertent data leaks without compromising productivity.

• Real-Time Browser DLP for AI Prompts

At the core of LayerX’s solution is its DLP (Data Loss Prevention) capability. Unlike legacy DLP tools that operate at the network or endpoint level, LayerX integrates directly into the browser—the primary interface for AI tools like ChatGPT. This allows it to inspect and control user input in real-time, before data ever leaves the enterprise perimeter. LayerX detects sensitive data such as PII, source code, financial details, or confidential documents when users attempt to paste or type it into ChatGPT. It then enforces policy-based actions, such as redaction, warning prompts, or outright blocking.

Outcome: Sensitive data is stopped at the source, preventing accidental or unauthorized exposure without interrupting the user’s workflow.

• Generative AI Monitoring and Shadow AI Visibility

LayerX provides full visibility into all GenAI tools, websites, and SaaS apps accessed by users, whether sanctioned or shadow. By continuously monitoring browser activity, it identifies who is using which AI tools and through which accounts – corporate, SSO, or personal. It also detects what kind of data is being input, whether they’re writing prompts, pasting customer data, or uploading sensitive files. 

Outcome: This allows security teams to detect unauthorized use, eliminate shadow AI, monitor sensitive data interactions, identify high-risk behavior and take corrective action before a data incident occurs.

• Granular, Context-Aware Policy Enforcement

With LayerX, enterprises can define context-aware policies tailored to AI use cases. Policies can be enforced at the browser level based on user role, app context, data type, and session attributes. For example, policies can allow marketing teams to use ChatGPT for content generation while blocking the submission of customer data or internal documents. Developers can be allowed to test code snippets but not share source code repositories. LayerX enforces policy-based actions, such as redaction, warning prompts to alert users when they’re about to violate a policy, or outright blocking.

Outcome: AI enablement and enterprise AI protection, ensuring responsible use without restricting innovation.

• Plugin and Extension Governance

LayerX also protects against risky AI plugin interactions, which can silently exfiltrate prompt content to third-party APIs. It identifies and categorizes AI browser extensions and plugins by risk level, source, and functionality. It also monitors and governs plugin behavior, giving admins the ability to approve, block, or restrict plugins based on their data handling practices. 

Outcome: Enterprises reduce their exposure to plugin-based vulnerabilities and enforce stronger AI data governance across the organization.

Conclusion: Enabling Safe, Scalable AI Across the Enterprise with LayerX

Generative AI is here to stay, and it’s reshaping how work gets done across every organization. But without the right safeguards, GenAI tools like ChatGPT can quickly turn from productivity boosters into data leakage risks. LayerX empowers enterprises to embrace AI confidently, with the visibility, control, and protection needed to keep sensitive data secure, usage compliant, and risk under control. Whether you’re battling shadow AI, enforcing AI usage policies, or preventing real-time data leaks, LayerX delivers the security foundation for safe and scalable AI adoption. 

Don’t let AI innovation outpace your security strategy. Adopt LayerX today and turn AI from a risk into a competitive advantage.

Request a demo to see LayerX in action.