Strengthening Enterprise Data Security with AI DataFirewall™ and DLP Integration

Robert Westmacott
Mar 30
6 min read

Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed business operations, driving efficiency and innovation. However, they also introduce a major security risk: data leakage.

Organizations inadvertently expose sensitive business information, personal information (PI), and intellectual property when interacting with AI models. Traditional Data Loss Prevention (DLP) strategies focus on securing endpoints, emails, and cloud storage but often fail to address AI specific vulnerabilities.

AI DataFirewall™ by Contextul integrates with existing DLP frameworks, ensuring real-time monitoring, filtering, and pseudonymisation of sensitive data before it interacts with AI systems.

This white paper explores how such technologies enhance DLP strategies, ensuring enterprises can leverage AI responsibly while maintaining regulatory compliance and robust data protection.

Introduction: The Rising Need for AI Specific Data Protection

The Growth of AI in Enterprise Environments

AI adoption in enterprises has skyrocketed, with applications spanning from content generation to automated decision-making. 82% of executives believe AI provides a competitive advantage, yet only 23% have adequate security policies in place to manage AI related data risks

(source: Stanford AI Index 2024).

The Security Risks of LLM Adoption

Despite their benefits, LLMs introduce new data security risks that traditional DLP systems struggle to address:

Unintentional Data Exposure – Employees input sensitive data into AI tools without understanding its storage or processing implications.
Regulatory Violations – AI models interacting with PII or confidential business data may violate GDPR, HIPAA, CCPA, and other global regulations.
Intellectual Property Leaks – Once proprietary data is shared with an AI tool, organizations lose control over where and how it is used.
Lack of Visibility and Control – Enterprises lack clear auditing and governance mechanisms for AI interactions.

A robust security strategy must include AI specific protections alongside traditional DLP measures

AI DataFirewall™: How It Works

AI DataFirewall™ by Contextul is a real-time security layer that scans, filters, and protects enterprise data before it reaches AI models.

Key Capabilities:

AI Specific Data Filtering – Detects and blocks sensitive information in text prompts and file attachments before it reaches external AI systems.
Pseudonymisation & De-Pseudonymisation – Masks sensitive data before AI processing and restores it upon retrieval, ensuring compliance with global regulations.
Policy-Based Access Control – Enforces company policies on which employees can use AI and what data can be shared.
Regulatory Compliance Engine – Supports GDPR, UK DPA 2018, HIPAA, CCPA, and 27+ other legal frameworks, preventing unauthorised data sharing.
Seamless Integration with AI Platforms – Works with ChatGPT, currently and will be extended to Claude, Co-Pilot, Gemini, and other AI models, ensuring safe enterprise AI adoption.
Enterprise-Grade Logging & Auditing – Maintains detailed logs of AI interactions, allowing compliance teams to track and analyse usage.

By implementing AI DataFirewalls, organizations can confidently use AI tools while eliminating the risk of data leaks, compliance violations, and intellectual property loss.

Integrating AI DataFirewall™ into an Enterprise DLP Strategy

The Role of DLP in Enterprise Security

Traditional DLP solutions are designed to monitor, detect, and prevent unauthorised data sharing across email, cloud storage, and endpoints. However, they lack AI specific protections for:

Unstructured data flowing into LLMs (via prompts and attachments)
Real-time AI interactions where sensitive data is unknowingly exposed
Regulatory controls for AI processing of enterprise data

By incorporating AI DataFirewall™ into current DLP tools, organizations can broaden their data security perimeter to encompass AI-driven workflows.

How AI DataFirewall™ Strengthens DLP

Traditional DLP	Enhanced with AI DataFirewall™
Monitors endpoints, emails, and file-sharing	Monitors AI prompts, responses, and attachments
Blocks known data categories (e.g., SSNs, credit cards)	Scans unstructured prompts for sensitive information
Prevents external sharing of confidential documents	Prevents confidential data from entering AI models
Focuses on policy-based access controls	Adds real-time AI query filtering and pseudonymization
Enforces encryption and data masking	Automates data redaction before AI interactions

Implementation Framework: AI DataFirewall™ + DLP

Assess AI Usage Risks: Conduct an AI security audit to identify sensitive data exposure points.
Integrate AI DataFirewall™ with DLP Systems: Connect AI DataFirewall™ to existing DLP infrastructure for holistic data protection.
Define AI Security Policies and establish governance frameworks specifying:
- What AI tools are allowed in the enterprise
- What types of data can interact with AI models
- Who can access AI capabilities
Enable Real-Time AI Data Filtering: configure AI DataFirewall™ to scan, redact, and anonymise sensitive data before it reaches AI models.
Monitor AI Interactions and Compliance Logs: Use audit trails to ensure policy adherence and regulatory compliance.
Continuously Update AI Security Policies: As AI evolves, periodically review and refine security controls.

Case Study: AI DataFirewall™ in Action

Industry: Legal Services

Challenge: A global law firm using Microsoft Purview as its core DLP strategy faced compliance risks as fee earners and paralegals used AI powered legal research and drafting tools, potentially exposing confidential client information.

Solution: The firm deployed AI DataFirewall™ within a secured Docker container, ensuring that all AI interactions were filtered before reaching external AI services while complementing Microsoft Purview’s DLP capabilities.

Technical Implementation:

Integration with Microsoft Purview:
- AI DataFirewall™ was configured to work alongside Microsoft Purview, which monitors document movement, endpoint activity, and email security.
- AI DataFirewall™ added a real-time filtering layer for AI interactions, ensuring that no confidential case data entered ChatGPT or other LLMs.
Policy Enforcement:
- Microsoft Purview enforced role-based access controls and data classification policies.
- AI DataFirewall™ ensured that even if an employee had access to case files, sensitive details were pseudoanonymised before interacting with AI models.
Monitoring & Auditing:
- Microsoft Purview provided a holistic data visibility dashboard, tracking all DLP events.
- AI DataFirewall™ generated audit logs of AI interactions, ensuring compliance with ABA regulations and GDPR.

Benefits of AI DataFirewall™ and Microsoft Purview Together

End-to-End Data Protection – Microsoft Purview covers traditional DLP, while AI DataFirewall™ extends security to AI based workflows.
Regulatory Compliance – Ensures GDPR, HIPAA, and ABA compliance by preventing unauthorised AI data exposure.
Enhanced Data Classification – Purview classifies documents, and AI DataFirewall™ prevents classified data from leaking into AI models.
Seamless AI Adoption – Lawyers can safely use AI for research and drafting without violating confidentiality agreements.

Outcome: Contextul’s AI DataFirewall™ leverages advanced pattern-matching techniques originally developed for their PrivacyManager™ DSAR platform, adapting these capabilities to address modern AI-related data risks. The PrivacyManager™ system uses sophisticated pattern recognition to identify personal information across documents during Data Subject Access Request (DSAR) processing, ensuring accurate redaction of third-party data while maintaining compliance. AI DataFirewall™ applies these proven pattern-matching methods in real-time AI interactions, scanning both text prompts and file attachments (Word, Excel, PDFs, etc.) for 27+ categories of sensitive information.

The law firm is maintaining client confidentiality, eliminated compliance risks, and allowed their lawyers to leverage AI securely while maximising Microsoft Purview’s DLP capabilities.

Regulatory Compliance & AI Governance

Key Regulations Addressed:

✅ GDPR – Prevents unauthorised AI processing of personal data

✅ HIPAA – Protects patient health information (PHI) from AI models

✅ CCPA – Ensures AI interactions comply with California privacy laws

✅ SOX & SEC Regulations – Prevents financial data exposure in AI systems

✅ ISO 27001 – Aligns with global information security best practices

AI DataFirewall™ ensures regulatory adherence by design, reducing legal risks associated with AI adoption.

Best Practices for AI Security Implementation

🔹 Classify AI Usage Risks – Identify where sensitive data may enter AI systems.

🔹 Use AI Specific Security Tools – Traditional security measures aren’t enough adopt

AI DataFirewall™ for dedicated protection.

🔹 Implement Real-Time Monitoring – AI interactions should be logged and analysed continuously.

🔹 Educate Employees on AI Security – Train teams on best practices for responsible AI usage. 🔹 Adopt a Zero-Trust AI Framework – Enforce strict access controls and least privilege access to AI systems.

Conclusion: Future-Proofing Enterprise AI Security

AI is reshaping industries, but its risks must be managed proactively. Enterprises that integrate AI DataFirewall™ into their DLP strategy gain a competitive edge by securing AI interactions without hindering innovation.

For CIOs, CTOs, and CISOs, AI security is no longer optional it’s a business imperative. The question isn’t whether to use AI, but how to use it securely, responsibly, and in compliance with global regulations.

Next Steps: Secure Your AI Strategy Today

Interested in safeguarding your enterprise AI usage? Contact Contextul today to explore how AI DataFirewall™ can integrate seamlessly into your security framework.