{
  "id": 16944491,
  "title": "AI gets keys to unmapped data: theCUBE’s AI Cybersecurity and Resilience Summit day-two kickoff analysis",
  "url": "https://urgent.news/2026/10/28/ai-gets-keys-to-unmapped-data-thecubes-ai-cybersecurity-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-28T19:03:23.000Z",
  "source": {
    "name": "SiliconANGLE",
    "slug": "siliconangle",
    "url": "https://siliconangle.com/2026/10/28/data-discovery-classification-anchors-ai-governance-aicybersecurityandresiliencesummit/"
  },
  "original_language": "en",
  "account": "Day two of the AI Cybersecurity and Resilience Summit kicked off with a discussion on the challenges of implementing AI trust and cyber resilience principles within organizations. Jon Oltsik, principal analyst in residence at SiliconANGLE and theCUBE, highlighted the importance of data discovery and classification as enterprises integrate AI into their operations. He explained that security teams must understand what information is accessible to AI systems and how access is governed, as turning AI trust and resilience into practical controls presents a significant challenge.\n\nOltsik and Krista Case, from theCUBE Research, emphasized that while writing policies is straightforward, enforcing them is far more difficult. This is where effective controls need to be put in place, starting with data hygiene. Oltsik noted that chief information security officers are primarily concerned with controlling AI within their organizations. However, he stressed that the principles of role-based access control and least privilege, which have long been used to protect regulated data, still apply. The difficulty lies in adapting these principles to the dynamic, non-deterministic world of AI, all while maintaining a pace that matches the scale and speed of AI advancements.\n\nOltsik pointed out that the level of autonomy granted to AI agents should be determined by each organization's risk tolerance. Similar to early intrusion prevention tools, agents that handle sensitive data or operate autonomously should still have human oversight. He emphasized the importance of gradually easing into these decisions, understanding the actions taken by agents or models, and developing a comfort level over time.\n\nTo ensure effective AI adoption, enterprises should begin by addressing the basics: data discovery and classification, identity and access management, and audits of overly permissive accounts, including those belonging to AI agents. Security leaders must also participate in the decision-making process regarding the business's AI adoption. Oltsik stressed that the security team should not be viewed as the \"no\" department, and that business objectives and goals must be aligned with appropriate risk management and risk mitigation strategies.",
  "summary": "Data discovery and classification are becoming important considerations as enterprises bring AI into their operations. Security teams need to understand what information is available to AI systems and how access is governed. Data discovery and classification are becoming important considerations as enterprises bring AI into their operations. Security teams need to understand what information is…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}