As AI adoption accelerates, so do the risks of data leakage and regulatory gaps.
Large Language Models (LLMs) do not store records like a traditional database; they learn patterns. Once sensitive data has influenced a model's weights, there is no standard way to 'erase' it without retraining the entire model, which is often complex and quite costly.
Furthermore, the rise of 'Shadow AI', where employees use unauthorized open-source models, creates visibility gaps which further complicates the problem.
Security leaders must recognize that traditional data protection is no longer sufficient for the AI landscape. Unlike standard applications, AI systems require continuous validation against unique adversarial inputs.
Key Capabilities for AI Security
Safeguard Sensitive Data in LLMs
Prevent sensitive corporate information from influencing public model weights, a process that is costly and complex to reverse. Leverage Data Security Posture Management (DSPM) to identify, classify, and control the flow of proprietary data before it enters the AI training pipeline or prompts.
Simplify AI Regulatory Compliance
Align your security posture with emerging frameworks and guidelines around the world, such as the EU AI Act, Australia's AI Ethics Principles, and India's Digital Personal Data Protection Act, ensuring your security teams have the detailed asset intelligence required to satisfy auditors and regulatory scrutiny.
Proactively Defend Against AI Threats
Move beyond traditional network security to address AI-specific vulnerabilities. Implement AI Red Teaming and Runtime Security to simulate adversarial scenarios, block prompt injections, prevent jailbreaks, and stop malicious tool use in real-time, ensuring your AI models remain resilient against manipulation.
Gain Total Visibility into AI Usage
Eliminate blind spots across your organization by establishing a comprehensive inventory of all AI assets, including third-party applications, models, and agents. Detect and manage "Shadow AI" to ensure sensitive data is never inadvertently exposed to unregulated, unsecured, or deprecated systems.