AI Security vs. Traditional DLP: Why Do Modern Businesses Need Both Approaches?

In the era of rapid generative artificial intelligence development, the threat landscape changes daily. For cybersecurity leaders and CISOs, it is becoming clear that legacy data leakage control methods can no longer cover the full spectrum of risks. Implementing generative AI into enterprise processes requires a revision of cybersecurity strategies. Today, data protection must be agile, adaptive, and intelligent.

Data protection evolution: From rigid rules to understanding context

For years, traditional Data Loss Prevention systems served as the foundation of enterprise security. They excelled at their tasks through rigid policies, regular expressions, and Exact Data Match (EDM) techniques. This is an ideal tool for protecting structured information: credit card numbers, passport data, and medical records—critical for compliance with GDPR, HIPAA, or PCI-DSS standards.

However, modern business generates vast amounts of unstructured data: source code, strategic plans, business ideas, and research findings. For attackers, this intellectual property is the primary target. This is where Forcepoint AI Security comes to the rescue—a solution that leverages Large Language Models (LLMs) and Natural Language Processing (NLP) to understand context and semantics.

Key differences

Traditional DLP

• Protected objects: Structured data (PII, PCI, PHI).
• Detection methods: Regular expressions, keywords, file fingerprinting.
• Main objective: Ensuring compliance and preventing regulatory violations.
• Limitations: High false positive rates when dealing with complex context.

AI security

• Protected objects: Unstructured data, intellectual property, source code.
• Detection methods: Machine learning, NLP, contextual and semantic analysis.
• Main objective: Protecting unique corporate assets and controlling generative AI usage.
• Benefits: Ability to understand document meaning even if rephrased.

Evolution and relevance of Forcepoint DLP

Despite the emergence of AI tools, classic Forcepoint DLP remains highly relevant. The vendor continues to actively develop this product, transforming it into a comprehensive platform that protects data everywhere: at endpoints, in the network, email, and cloud applications (aligned with CASB). One of the key innovations is the introduction of Unified Policies, allowing administrators to define a rule once and automatically enforce it across all data channels.

The Risk-Adaptive Protection capability has elevated Forcepoint DLP to a new level. By analyzing user behavior, the system dynamically adjusts policy enforcement based on an individual employee's risk score. If a user exhibits suspicious behavior, the system automatically blocks data transfer, while trusted employees continue working without friction.

Capabilities of Forcepoint AI Security

Integrating artificial intelligence into the Forcepoint ecosystem unlocks new horizons. The Forcepoint AI Security platform provides CISOs with Data Security Posture Management (DSPM) tools that automatically discover, classify, and prioritize data risks in cloud environments (AWS, Azure, Google Cloud). AI helps locate shadow data and mitigate vulnerabilities before a breach occurs.

Moreover, Forcepoint ensures the secure adoption of ChatGPT and other GenAI tools within the company. The system analyzes employee prompts and blocks accidental or intentional attempts to submit sensitive information to public neural networks.

For reliable enterprise data protection, CISOs must leverage the synergy of classic DLP and AI tools, ensuring total control over all data types.

Why choose Softprom

Implementing comprehensive security systems requires deep expertise. Softprom is an official distributor of Forcepoint solutions. Our team of certified engineers provides end-to-end support at all stages: from initial audit and sizing to implementation, integration, and staff training.