CyberHappenings logo
☰
Home Browse About

Track cybersecurity events as they unfold. Sourced timelines, daily updates. Fast, privacy‑respecting. No ads, no tracking.

AI Data Security Buyer's Guide for Enterprises

First reported
Last updated
1 unique sources, 1 articles

Summary

Hide ▲

A new buyer's guide addresses the challenges of securing AI data in enterprises. The guide emphasizes the need for a new mental model to evaluate AI data security solutions, as traditional controls are inadequate. It outlines a counterintuitive buyer's journey that focuses on real-time monitoring, nuanced enforcement, and architecture fit. The guide also highlights the importance of balancing security and productivity, and the need to consider both technical and non-technical factors in the evaluation process. The guide aims to help security leaders navigate the crowded AI data security market and make informed decisions about AI data security solutions. It provides a framework for evaluating solutions based on their ability to understand and control AI usage at the last mile, and their adaptability to new AI tools and compliance regimes.

Timeline

  1. 17.09.2025 14:03 1 articles · 12d ago

    AI Data Security Buyer's Guide Published

    A new buyer's guide addresses the challenges of securing AI data in enterprises. The guide outlines a counterintuitive buyer's journey that focuses on real-time monitoring, nuanced enforcement, and architecture fit. It provides a framework for evaluating AI data security solutions based on their ability to understand and control AI usage at the last mile. The guide also highlights the importance of balancing security and productivity, and the need to consider both technical and non-technical factors in the evaluation process.

    Show sources
    Open in new tab

Information Snippets

  • Generative AI tools have become integral to enterprise productivity, but their rapid adoption has created new security challenges.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • Traditional security controls are not designed to cover the risks associated with AI data usage.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • The AI data security market is crowded, with vendors rebranding legacy solutions as AI security.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • The buyer's journey for AI data security should focus on discovery, real-time monitoring, enforcement, and architecture fit.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • Effective AI data security requires nuanced enforcement, such as redaction and just-in-time warnings, rather than binary allow/block controls.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • Non-technical factors, such as operational overhead and user experience, are crucial for the success of AI data security solutions.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

  • The best AI security investments enable safe AI usage rather than blocking all AI tools.

    First reported: 17.09.2025 14:03
    1 source, 1 article
    Show sources

Similar Happenings

AI Governance Strategies for CISOs in Enterprise Environments

Chief Information Security Officers (CISOs) are increasingly tasked with driving effective AI governance in enterprise environments. The integration of AI presents both opportunities and risks, necessitating a balanced approach that ensures security without stifling innovation. Effective AI governance requires a living system that adapts to real-world usage and aligns with organizational risk tolerance and business priorities. CISOs must understand the ground-level AI usage within their organizations, align policies with the speed of organizational adoption, and make AI governance sustainable. This involves creating AI inventories, model registries, and cross-functional committees to ensure comprehensive oversight and shared responsibility. Policies should be flexible and evolve with the organization, supported by standards and procedures that guide daily work. Sustainable governance also includes equipping employees with secure AI tools and reinforcing positive behaviors. The SANS Institute's Secure AI Blueprint outlines two pillars: Utilizing AI and Protecting AI, which are crucial for effective AI governance.

Open in new tab