Generative artificial intelligence is transforming how businesses operate. Large language models can draft emails, summarise documents and even generate code, while image generation tools create marketing materials and product mock ups with astonishing speed. This rapid adoption is creating enormous value, but it is also exposing critical gaps in governance and data protection. Chief information security officers and compliance officers find themselves at a crossroads: they must accelerate the adoption of generative AI while ensuring that sensitive data is not exposed, misused or misinterpreted. Traditional data loss prevention strategies were designed for a different era—an era of on premises servers, file transfers and predictable user behaviour. Today, data moves at the speed of chat interfaces and web browsers. Sensitive information can easily be pasted into a conversational agent or summarised by a generative model without anyone noticing. To manage these risks, organizations need a new governance model and the right tools to enforce it.
Netrix Global, a long standing Microsoft partner with deep expertise in data intelligence and cybersecurity, helps organizations navigate this new landscape. By combining advanced Microsoft Purview capabilities with proven governance frameworks, Netrix enables clients to protect their data while unlocking the power of generative AI. This blog explores why traditional data loss prevention falls short in the era of generative AI, highlights the regulatory pressures driving change and outlines practical steps for closing governance gaps. Along the way, it showcases how Netrix Global and Microsoft Purview can help organizations build a resilient, compliant foundation for innovation.
Generative AI adoption has exploded in recent years. Organizations across industries are deploying chat assistants, copilots and content generators to automate work and spark creativity. This growth is driven by the promise of efficiency gains and competitive advantage. However, generative models are only as safe as the data and guardrails that shape them. Without proper controls, these systems can inadvertently expose confidential information or amplify existing biases. For example, a generative AI tool might summarise a document that contains sensitive client details or share internal pricing strategies in response to a seemingly innocuous prompt. Another scenario involves employees copying data from business applications into an AI chat interface to get quick answers, unknowingly violating company policies and regulations. These behaviours create new attack surfaces for malicious actors and heighten the risk of data breaches.
Studies illustrate the magnitude of the problem. A widely cited report from the data protection space notes that seventy percent of enterprise data leaks now happen directly in the browser, and more than fifty percent involve actions such as copying data into chat applications or AI prompts. Traditional data loss prevention tools, which focus on scanning files and monitoring network traffic, are blind to these in browser actions. The same report points out that more than half of employees use unapproved SaaS applications, creating channels where sensitive data can leak without oversight. These statistics underscore how modern work patterns—web apps, collaboration platforms and generative AI tools—have outpaced legacy security controls.
Generative AI also introduces unique attack vectors. Models can be “prompt injected” by malicious actors to reveal training data or internal information. Bad actors might attempt to feed toxic or adversarial prompts to manipulate outputs or extract confidential content. In the rush to adopt AI, organizations sometimes neglect proper governance, focusing on quick wins instead of sustainable risk management. This environment makes it easy for mistakes to occur. When a model returns or summarises information that should have remained confidential, it can lead to noncompliance with data protection laws, reputational damage and legal exposure. Thus, as generative AI becomes mainstream, organizations must re-evaluate their governance strategies.
Traditional data loss prevention (DLP) solutions were built for an era of perimeter based security. They excel at monitoring network boundaries, scanning files for sensitive patterns and blocking suspicious transfers via email or external drives. But generative AI and modern SaaS applications have shattered these boundaries. Employees interact with AI tools directly through web browsers, and data flows through copy paste actions, API calls and third party integrations. Many of these interactions do not involve file transfers at all. Instead, users might copy text from a database, paste it into a generative assistant to summarise, then share the output in a chat or email. Traditional DLP solutions often miss these interactions because they focus on the endpoint or the network perimeter, not the web session itself.
The limitations of legacy DLP become clear when we consider the statistics: seventy percent of data leaks now happen directly in the browser, making them invisible to endpoint or network based DLP tools. Furthermore, fifty three percent of these leaks involve copying data into chat applications or AI prompts, a behaviour that traditional tools struggle to monitor. There are several reasons for this failure:
Because of these limitations, organizations relying solely on traditional DLP may discover leaks only after the damage is done. To close the gap, they need a more comprehensive approach that includes browser based monitoring, proactive governance and real time user coaching.
The risks associated with generative AI are not only operational but also legal. Laws around the world increasingly emphasise data privacy and hold organizations accountable for how they collect, process and share personal information. The United States, European Union, Asia Pacific region and others have adopted stringent regulations requiring organizations to protect sensitive data and notify authorities of breaches. Generative AI complicates compliance because models can inadvertently store or reveal personal information. The NIST Generative AI Risk Management Framework highlights “Data Privacy” as a top risk category, defining it as the impact of leakage, unauthorised use or de anonymisation of personally identifiable information or sensitive data. In other words, if an AI model or its prompts expose personal data, organizations could face regulatory investigations and fines.
Regulators are already scrutinising AI deployments. Recent guidelines emphasise transparency, fairness and accountability in AI systems. They require organizations to implement safeguards against data leaks, biases and misuse. For example, the European Union’s General Data Protection Regulation (GDPR) includes provisions for automated decision making and mandates that personal data be protected throughout its lifecycle. Many industry regulators—such as those in healthcare, financial services and education—have sector specific rules around data handling that apply equally to AI systems. Failing to address governance gaps could not only violate these regulations but also erode customer trust.
This regulatory pressure makes it imperative for CISOs and compliance officers to implement a robust governance strategy that spans data discovery, classification, access management and monitoring. It is not enough to rely on existing DLP policies; organizations must adopt AI specific controls to ensure that models do not process or output data they shouldn’t. Effective governance also means documenting decision making processes, conducting risk assessments and demonstrating compliance to regulators. To meet these obligations, organizations need modern tools that provide visibility into AI prompts, monitor AI outputs and enforce policies in real time.
Recognising the shortcomings of traditional DLP and the heightened regulatory environment, organizations must build a new governance model tailored to generative AI. This model should encompass technical controls, processes and cultural practices that work together to protect data. Key elements include:
Microsoft Purview is a suite of data governance and compliance tools that provide holistic protection across data, applications and AI workloads. It includes Data Security Posture Management (DSPM) for AI, sensitivity labels, Data Loss Prevention, insider risk management and integration with the wider Microsoft security ecosystem. These features help organizations implement the governance model described above.
DSPM for AI helps organizations discover where oversharing is occurring and identify gaps in sensitivity labels and DLP policies. It provides insights and analytics to monitor and protect data in AI prompts. DSPM includes graphical dashboards and reports that visualise AI interactions, show which prompts access sensitive data and highlight high risk users or applications. Security teams can use this information to fine tune policies and remediate oversharing. Organizations can start with the “Assessment for the week” report to see which sites or services are oversharing, then implement recommendations to improve security.
Purview’s sensitivity labels enable organizations to classify data based on sensitivity and apply appropriate access controls. Labels can restrict AI apps from returning data to users who lack the required permissions. For example, if a file is marked Highly Confidential, AI models integrated with Purview will not summarise or share it unless the user has clearance. Labels also travel with data as it moves across applications, ensuring consistent enforcement. For AI generated content, labels can automatically be applied to outputs, helping maintain compliance. Classification is a foundational capability that ensures data is treated according to its importance and regulatory requirements.
Purview includes DLP policies tailored for AI workloads. These policies inspect user prompts and AI outputs in real time, scanning for sensitive information like credit card numbers or health records. If a user attempts to paste such information into an AI chat, the policy can warn them or block the action. DLP can also restrict AI models from summarising highly confidential files or generating responses that contain personal data. This real time enforcement helps prevent data leakage before it happens.
The final phase involves developing a detailed roadmap to close governance gaps. Netrix collaborates with clients to design and deploy solutions that leverage Microsoft Purview, DLP, Defender for Cloud and other security tools. The plan includes timelines, milestones, budgets and success metrics. During implementation, Netrix’s engineers integrate the tools into the existing environment, configure policies and train staff. Post implementation, Netrix provides ongoing support through managed services, ensuring that policies remain effective as new AI capabilities are introduced.
Netrix’s holistic approach, combined with its status as a Microsoft partner, enables organizations to adopt generative AI with confidence. Clients benefit from a single partner who can advise on strategy, implement technology and manage operations around the clock.
While every organisation is unique, the following steps provide a structured path for CISOs and compliance officers seeking to close governance gaps in the generative AI era:
By following these steps, organizations can create a resilient governance framework that supports innovation without compromising security.
Generative AI promises transformative benefits but also introduces new risks and governance challenges. Traditional data loss prevention tools cannot keep pace with the ways data moves through modern browsers, SaaS applications and AI prompts. CISOs and compliance officers must therefore adopt a new governance model—one that emphasises data discovery, least privilege access, classification, real time monitoring and continuous improvement. Regulatory pressures and public scrutiny make this task even more urgent.
Microsoft Purview provides the technological foundation to close these governance gaps. Its DSPM for AI, sensitivity labels, DLP policies and insider risk management capabilities allow organizations to monitor AI interactions, classify and protect data and respond to threats in real time. When combined with Microsoft Defender’s AI threat protection, the result is a comprehensive security stack tailored for the generative AI era. However, tools alone are not enough. A partner like Netrix Global can guide organizations through assessment, planning and implementation, ensuring that governance strategies align with business objectives and regulatory requirements. With the right combination of tools, processes and expertise, organizations can embrace generative AI confidently, safeguarding sensitive information while unlocking new opportunities for innovation.
Traditional DLP focuses on monitoring file transfers and network traffic. Generative AI tools operate in web browsers where users copy text and interact with chat interfaces. Studies show that most modern leaks occur directly in browsers, and many involve copy paste actions into AI prompts. Legacy DLP tools do not inspect these sessions or provide real time coaching, leaving organizations exposed. To address generative AI risks, companies need browser centric monitoring, real time policies and AI specific controls.
An effective AI governance program includes data discovery and classification, least privilege access controls, sensitivity labels, real time monitoring, insider risk management, incident response planning and continuous employee training. These elements work together to ensure that AI tools only access appropriate data, that sensitive information is labeled and protected and that risky behaviour is detected and addressed promptly. Governance also involves aligning AI initiatives with regulatory requirements and ethical guidelines.
Netrix Global is a Microsoft partner and member of the Microsoft Intelligent Security Association. The company has extensive experience in data governance, cybersecurity and AI readiness. Netrix guides clients through assessments of their current environment, conducts workshops to explore AI possibilities and develops detailed plans to implement governance controls. By leveraging Microsoft Purview and Defender solutions, Netrix helps organizations classify data, set up DLP and insider risk policies and monitor AI interactions. Netrix also provides ongoing managed services to keep policies current as technologies evolve.
CISOs can begin by performing a data inventory and classification exercise. Applying sensitivity labels to critical data sets and deploying basic DLP rules to prevent copying of sensitive content into AI prompts will yield immediate benefits. Enabling SharePoint oversharing controls and setting up a DSPM assessment provide quick visibility into oversharing issues. Training employees about AI risks and establishing incident response playbooks are other quick wins. Engaging an experienced partner like Netrix ensures that these actions fit into a broader strategy and that they are implemented effectively.
