Copilot DLP

Protect Sensitive Data Entered into MS Copilot

Table of Contents

TL;DR:

AI assistants like MS Copilot and ChatGPT offer productivity gains but also pose data security risks.
Copilot DLP is crucial for safeguarding sensitive data from exposure and compliance breaches.
Implementing Copilot DLP involves strategies like input filtering, output monitoring, and access controls.
Strac CoPilot DLP integration offers real-time monitoring, customizable policies, and compliance management.
Organizations should develop clear policies, educate employees, and collaborate with IT and security teams to maximize the benefits of AI assistants while minimizing risks.

In today's fast-paced digital landscape, AI assistants like GitHub Copilot and ChatGPT have revolutionized the way we work, offering unprecedented levels of productivity and efficiency. These tools leverage machine learning to provide intelligent suggestions, automate routine tasks, and enhance decision-making processes. However, with great power comes great responsibility. As organizations increasingly rely on AI assistants, the risk of sensitive data exposure and compliance breaches escalates. This is where Copilot Data Loss Prevention (DLP) comes into play—a crucial strategy to safeguard your organization's most valuable asset: its data.

The Rise of AI Assistants and Associated Risks with Copilot DLP

AI assistants have become indispensable in various domains:

Software Development: GitHub Copilot assists developers by suggesting code snippets and functions.
Customer Service: Chatbots handle customer inquiries, providing instant support.
Content Creation: Tools like ChatGPT generate human-like text for blogs, reports, and more.

While these assistants offer significant benefits, they also introduce new vulnerabilities:

Data Leakage: AI models trained on vast datasets may inadvertently expose sensitive information.
Compliance Violations: Sharing confidential data with AI tools can breach regulations like GDPR, HIPAA, or CCPA.
Intellectual Property Risks: Proprietary code or business strategies could be unintentionally disclosed.

Notable Incidents

Samsung Data Leak (2023): Employees inadvertently shared sensitive code with an AI assistant, leading to a potential breach of intellectual property.
Healthcare Data Exposure: Misuse of AI tools led to the accidental sharing of patient information, violating HIPAA regulations.

Understanding Data Loss Prevention (DLP)

Data Loss Prevention (DLP) refers to strategies and tools designed to prevent unauthorized access, misuse, or exfiltration of sensitive data. DLP solutions monitor and control data flows across an organization, ensuring compliance with regulatory requirements and internal policies.

Key Components of DLP

Data Identification: Classifying data based on sensitivity and regulatory requirements.
Monitoring: Tracking data in motion, at rest, and in use.
Policy Enforcement: Applying rules to prevent unauthorized data actions like Alert (Warn), Block, Redact, Pseudonymize
Incident Response: Providing alerts and remediation steps for potential breaches.

The Necessity of Copilot DLP

As AI assistants become integral to business operations, integrating DLP into these tools is essential.

Why Traditional DLP Isn't Enough

AI Interactions are Complex: AI models process and generate data in ways traditional DLP solutions may not detect.
Real-Time Data Exchange: AI assistants often require real-time access to data, increasing the risk of instant leaks.
User Behavior: Employees might unknowingly input sensitive information into AI tools.

Benefits of Copilot DLP

Enhanced Security: Protects against inadvertent data exposure through AI assistants.
Regulatory Compliance: Ensures that interactions with AI tools adhere to legal standards.
User Awareness: Educates employees on safe AI usage practices.

Implementing Copilot DLP: Solutions and Strategies

Implementing Copilot DLP involves integrating DLP capabilities directly into AI assistants or their workflows.

Strategies

Input Filtering: Scanning data before it's processed by the AI assistant to prevent sensitive information from being input.
Output Monitoring: Analyzing the AI's responses for potential data leaks before presenting them to the user.
Access Controls: Restricting who can use AI assistants and what data they can access.
Encryption: Ensuring data transmitted to and from AI assistants is encrypted.

Technological Solutions

API Integration: Embedding DLP functions into AI assistant APIs.
Machine Learning Models: Training models to recognize and handle sensitive data appropriately.
Cloud Security Platforms: Utilizing cloud-based DLP solutions that work seamlessly with AI services.

Strac CoPilot DLP

Features of Strac Copilot DLP Solution

Seamless Integration: Easily connects with existing AI tools without disrupting workflows.
Real-Time Monitoring: Provides instant detection and blocking of sensitive data transfers.
Customizable Policies: Allows organizations to define what constitutes sensitive data.
Compliance Management: Helps maintain adherence to regulations such as GDPR, HIPAA, and CCPA.

Benefits

Reduced Risk of Data Breaches: Proactively prevents data leaks through AI assistants.
Operational Efficiency: Maintains the productivity benefits of AI tools while enhancing security.
User Education: Encourages responsible AI usage among employees.

Best Practices for Organizations

To maximize the benefits of AI assistants while minimizing risks, organizations should:

Implement Copilot DLP Solutions: Integrate tools like Strac.io's DLP to monitor and control data interactions.
Develop Clear Policies: Establish guidelines on acceptable use of AI assistants.
Educate Employees: Provide training on the risks associated with AI tools and how to use them securely.
Regular Audits: Conduct periodic reviews of AI interactions to detect potential vulnerabilities.
Collaborate with IT and Security Teams: Ensure that all stakeholders are involved in implementing and maintaining DLP measures.

Sensitive Data Types for Copilot DLP

Checkout all the sensitive data elements supported by Strac: https://www.strac.io/blog/strac-catalog-of-sensitive-data-elements

Conclusion

AI assistants like GitHub Copilot and ChatGPT are transforming the way we work, offering significant productivity gains. However, they also introduce new risks related to data security and compliance. Copilot DLP emerges as a critical strategy to mitigate these risks, ensuring that organizations can harness the power of AI without compromising on data protection.

By implementing solutions like Strac.io's integration and adhering to best practices, organizations can create a secure environment where AI assistants and data protection coexist harmoniously. As we continue to advance into the age of AI, prioritizing data security will be paramount in maintaining trust and achieving sustainable growth.

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Sharepoint DLP Use Cases

Practical Scenario

A hospital’s billing and administrative teams use SharePoint Online to store patient invoices, medical reports, and insurance forms. While collaborating with external insurance providers, a staff member accidentally updates the permissions on a SharePoint document library to “Anyone with the link,” exposing potentially thousands of patient files containing PHI.

How Strac's Sharepoint DLP Helps

Continuous Data Discovery: Strac automatically scans existing and newly uploaded documents, identifying PHI (e.g., medical record numbers, Social Security Numbers).
Classification & Labeling: Once identified, files are labeled (e.g., “HIPAA Sensitive”), ensuring that administrators know which documents require the highest level of protection.
Visibility into Access: Strac provides real-time insight into who has access to these sensitive documents. Administrators can instantly see if unauthorized users or broad groups have viewing rights.
Revoke Public Links: If a file is publicly accessible, Strac immediately revokes those links and restores restricted access.
Alerts & Quarantines: When someone attempts to share PHI externally, Strac can alert admins, quarantine the file for review, or completely block the action.
Audit-Ready Reports: All actions are logged, enabling quick incident response and demonstrating HIPAA compliance for audits.

Practical Scenario

A mid-sized investment firm uses SharePoint to collaborate on various client files, including:

Credit card statements (subject to PCI-DSS)
ID documents (Driver’s Licenses, Passports, etc.) used for KYC (Know Your Customer) verification
Banking information such as account and routing numbers

An associate accidentally shares a SharePoint folder containing these files with a newly onboarded client who does not require access to all confidential documents. This folder is also accessible to several internal teams outside the immediate project, creating multiple potential exposure points.

How Strac's Sharepoint DLP Helps

Comprehensive Data Discovery: Strac scans both existing and newly uploaded documents in SharePoint for sensitive information such as credit card numbers, bank account details, and ID documents (Driver’s License, Passport formats).
Classification & Automated Labeling: Once identified, Strac applies meaningful labels (e.g., “PCI-DSS Sensitive,” “PII – ID Documents,” “Banking Info”) to ensure these files stand out and are subject to stricter security rules.
Visibility into Access: Strac provides an immediate view of who currently has access to these sensitive files. This allows admins to spot situations where external clients or internal teams unnecessarily have permissions.
Public Access Revocation: If a labeled document (e.g., containing card data or ID scans) is found to be publicly shared or too broadly accessible, Strac automatically revokes these links or permissions, aligning access with the principle of least privilege.
Alerts, Quarantines, and Blocks: When a user attempts to share a labeled document with outside domains—or with an entire department—Strac alerts administrators or quarantines/blocks the file share, depending on policy settings.
In cases where the share is intentional but needs review, admins can approve or deny the request within Strac’s dashboard.
Audit & Compliance: Every sharing event, label assignment, and access revocation is logged, creating a detailed audit trail. This helps demonstrate compliance with PCI-DSS, KYC, AML, and other regulatory requirements.
Automatic reporting simplifies any regulatory or internal compliance audit, reducing the administrative burden on security and compliance teams.

Practical Scenario

A software company keeps source code, product roadmaps, and design specs in SharePoint. Several teams—including external contractors—use the same SharePoint site. A developer accidentally grants a large group, including some non-disclosure–exempt contractors, access to a folder containing patent-pending code.

How Strac's Sharepoint DLP Helps

Holistic File Scanning: Strac inspects documents, PDFs, and archives for code snippets, system designs, and proprietary business terms to detect potential IP.
Intelligent Labeling: Documents identified as containing IP or trade secrets are automatically classified (e.g., “Proprietary IP”), reinforcing the need for restricted sharing.
Real-Time Access Insights: With Strac, administrators can instantly see who has access to IP-tagged files, enabling them to remove unauthorized users or reduce permission scopes.
Immediate Link Removal: If a contractor or external partner is mistakenly granted access to IP, Strac revokes public or unauthorized sharing before the files can be downloaded.
Alerts & Blocking: Strac’s policies can be configured to alert security teams or block external sharing attempts for files containing proprietary content.
Incident Response & Auditing: Detailed logs of every share request, label change, and access revocation aid in quick incident resolution and help prove due diligence if legal issues arise.