Quick Summary
MIND has introduced DLP for Agentic AI, a data-centric security solution aimed at helping organizations manage sensitive data interactions with AI systems. This approach addresses risks associated with AI’s ability to autonomously create and share data across various platforms. By ensuring that sensitive data is properly governed and protected, MIND aims to facilitate safe AI adoption and drive business innovation.
The Securityish Brief
MIND has announced its DLP for Agentic AI, which focuses on securing sensitive data interactions within AI systems. This product is designed to help organizations safely harness the benefits of agentic AI, which can autonomously create, access, and share data across various platforms, including SaaS applications and third-party tools. The introduction of this solution comes in response to the increasing need for robust data security measures as organizations seek to leverage AI for innovation.
According to Eran Barak, CEO of MIND, organizations are not merely experimenting with AI; they are adopting it to drive tangible outcomes. This necessitates a strong foundation of data protection, governance, and understanding. MIND’s DLP for Agentic AI emphasizes the importance of ensuring that AI systems have appropriate access to sensitive data at all times.
The DLP solution allows organizations to identify active AI agents across their systems, detect risky data access, and monitor AI behavior in real time. This proactive approach enables security teams to autonomously alert and remediate issues as they arise, ensuring that data and AI systems interact safely without hindering productivity.
Why Data Security is Essential for AI
As organizations increasingly evaluate how to secure agentic AI, many emerging security approaches fail to adequately protect the critical data that AI relies on. MIND’s DLP for Agentic AI addresses this gap by prioritizing data security and controls, which are essential for successful AI adoption and measurable business results.
Zac Fletcher, Assistant Vice President of IT Security at Service Corporation International, highlighted the importance of balancing AI efficiencies with data security. He noted that MIND’s platform provides the necessary visibility and capabilities to maintain this balance, allowing organizations to benefit from AI without compromising sensitive data.
- MIND’s DLP for Agentic AI: A solution designed to protect sensitive data used by AI agents.
- Eran Barak: CEO of MIND, emphasizes the need for data protection in AI adoption.
- Zac Fletcher: Assistant VP of IT Security at Service Corporation International, advocates for balancing AI efficiencies with data security.
- Agentic AI: Refers to AI systems that can autonomously create, access, transform, and share data.
Key Takeaways
- Evaluate your organization’s current data security measures to ensure they align with AI adoption strategies.
- Implement monitoring tools to detect risky data access by AI agents in real time.
- Establish clear governance policies for sensitive data that AI systems will access.
- Train your security team on the specific risks associated with agentic AI and how to mitigate them.
- Regularly review and update your data protection protocols to adapt to evolving AI technologies.
Key Terms & Concepts
- Agentic AI: In this article, Agentic AI refers to AI systems that can autonomously create, access, transform, and share data across various platforms.
- DLP: DLP stands for Data Loss Prevention, a strategy to protect sensitive data from unauthorized access and breaches.
Read the original article here: https://www.helpnetsecurity.com/2026/01/28/mind-dlp-for-agentic-ai/
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