Best Practices of AI-Based Microsoft Purview DLP Implementation

Introduction

Data Loss Prevention (DLP) solutions are designed to prevent data from being disclosed, shared, or accessed by unauthorized individuals. Due to the rise in cyber threats, traditional DLP solutions may sometimes be insufficient. AI can improve the efficiency, precision, and intelligence of DLP systems in protecting data. It is essential to ensure that sensitive data within AI systems is also protected. 

Here are some statistics highlighting the importance of data protection in AI-driven applications handling unguarded company data.

• Increase in Cyberattacks: Cyberattacks using stolen or compromised credentials increased by 71% year-over-year 
• Sensitive Data Exposure: Nearly half (46%) of all breaches involve customer personal identifiable information (PII), such as tax identification numbers, emails, phone numbers, and home addresses 
• Shadow Data: 1 in 3 data breaches in 2024 involved shadow data, which is data that exists outside the company’s centralized data management system and is not managed or controlled by the IT team 
• Human Element: 74% of all breaches include the human element, such as accidental exposure or insider misuse 
• Third-Party Risks: 98% of organizations have at least one third-party vendor that has suffered a data breach 

Source: secureframe

Data exfiltration in AI platforms often results from unrestricted access, poorly managed third-party integrations, and inadequate real-time data governance. These risks underscore the need for strong security measures and ongoing monitoring to safeguard sensitive information.

The importance of AI in Microsoft DLP

Real-Time Protection: Traditional DLP systems use pre-defined rules to protect sensitive information, but evolving threats require more adaptive solutions. Real-time protection DLP uses AI and machine learning to monitor and analyze data continuously, detecting and responding to threats as they happen. 

Scalability and Adaptability: Scalability allows DLP systems to handle increasing data volumes and users without compromising performance. This includes managing large data volumes, supporting a growing workforce, and integrating with other security systems. Adaptability enables DLP systems to respond to emerging threats, comply with new regulations, and adjust to organizational changes. AI and machine learning enhance DLP solutions, making them proactive and adaptive to new risks. 

Reducing False Positives: One of the challenges faced by traditional Data Loss Prevention (DLP) systems is the occurrence of false positives, where legitimate data activities are mistakenly flagged as threats. This can lead to unnecessary disruptions in business operations and create a sense of frustration among users. However, AI and machine learning can significantly reduce the incidence of false positives in DLP systems.

Data Loss Prevention (DLP) for Microsoft 365 Copilot

Oversharing and data leakage are critical concerns for organizations adopting generative AI technologies, such as Microsoft 365 Copilot. According to a source: Data Index Report, 2024, 80% of business leaders identify data leakage by employees using AI as their primary concern regarding generative AI adoption.

DLP for M365 Copilot enables data security admins to exclude documents with specific sensitivity labels from being summarized or used in responses. This feature, compatible with Office files and PDFs in SharePoint, ensures that sensitive content remains secure and isn’t copied or processed by M365 Copilot.

DLP for Microsoft 365 Copilot includes features that allow data security administrators to prevent documents with specific sensitivity labels from being summarized or used in responses. This restriction applies to both Office files and PDFs stored in SharePoint, ensuring that sensitive information remains secure and is not inadvertently shared or processed by Copilot. For instance, this feature would prevent confidential legal documents or those marked as “Internal only” from being improperly accessed or summarized, thereby maintaining the integrity and confidentiality of such data.

AI-Driven DLP Data Investigation

To provide integrated insights across the security stack. This includes data risk insights related to specific assets targeted in potential cyberattacks. Within Security Copilot, we access detailed information on the volume and location of exfiltrated files, as well as the content within these files, such as credit card numbers or login credentials. Additionally, the sensitivity level of the files, including classifications like highly confidential, is also available.

We can efficiently prioritize events by evaluating DLP logs according to our specific requirements prompting AI.

Following here are the examples

• Summarize the latest Data Loss Prevention (DLP) alerts from Microsoft Purview
• Retrieve and organize the most recent DLP alerts for triage purposes
• Show me the top 5 DLP alerts that I should prioritize today

Best Practices to Strengthen DLP for AI

We must follow best practices to enhance AI DLP. According to a 2024 study, implementing encryption at both the data and transmission levels can reduce data breaches by up to 40%. Regularly updating security protocols and conducting vulnerability assessments can lower potential security risks by 25%. Additionally, training staff in cybersecurity awareness has been shown to decrease phishing attack success rates by 30%. It is also essential to use multi-factor authentication and maintain strict access controls to protect sensitive information. Adhering to these best practices not only safeguards data but also ensures compliance with regulatory standards.

Below is the phased approach:

Implementing Robust DLP Solutions

By implementing robust Data Loss Prevention (DLP) solutions enhanced with AI, your organization can proactively safeguard its most sensitive information. Elevate your data protection strategy, achieve compliance, and secure your digital assets.

Conclusion

In selecting the right DLP solution, having a structured approach is crucial for long-term success. Netwoven’s Delivery Model, as illustrated above, ensures a seamless journey from assessment to optimization. Through carefully tailored phases — from understanding your current data landscape to designing and implementing robust security measures, and continuously optimizing for effectiveness — Netwoven delivers comprehensive solutions that align with your unique business needs. Whether you choose Microsoft DLP or a third-party solution, this proven methodology helps maximize protection, enhance compliance, and empower your organization with a resilient data security framework. Let’s work together to build a resilient and secure digital environment!