{
  "id": 17199902,
  "title": "The imperative to prepare for AI-safe data security",
  "url": "https://urgent.news/2026/10/30/the-imperative-to-prepare-for-ai-safe-data-security",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T07:41:55.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/the-imperative-to-prepare-for-ai-safe-data-security"
  },
  "original_language": "en",
  "account": "Businesses are rapidly integrating AI tools into their operations, necessitating a shift in data security strategies. Gartner predicts that 40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, highlighting the urgency for organizations to prepare for this transformation. Traditional security measures, such as encryption and perimeter defense, are no longer sufficient to combat the evolving risks posed by AI-driven threats.\n\nThe nature of AI security has evolved from a simple focus on encryption and perimeter defense to a multi-dimensional challenge encompassing algorithmic trust, model integrity, and synthetic data exposure. Trust boundaries are shifting, and many companies are ill-equipped to handle these changes. Even the creators of AI have proven its potential for misuse, as evidenced by the Hugging Face breach, in which a rogue AI agent caused security issues.\n\nThe future risks associated with autonomous AI agents capable of \"emergent\" behavior pose significant challenges for maintaining security. Cybersecurity investment statistics indicate that businesses are vastly underprepared for these new threats. While the estimated annual cost of cybercrime is $10.5 trillion globally and projected to reach $15.63 trillion by 2029, this far exceeds Gartner's information security spend prediction of $244 billion in 2026.\n\nMachine data exacerbates the data quality issue, as \"garbage in, garbage out\" remains a concern. This underscores the need for technology and trust architectures to be redesigned, with internet security embedded into the data lifecycle. Security leaders must make considered investments in AI-safe security, transparency, and federated governance to protect their enterprise ecosystems.\n\nCurrent security risks are increasingly AI-driven, making it unlikely for any single enterprise to have comprehensive protection. Polymorphic, AI-generated attacks are becoming more frequent and sophisticated, surpassing human countermeasures. Deepfake technology, for example, can be generated quickly and cheaply to create malicious bots that can infiltrate systems and target sensitive data. Attackers can manipulate AI models by compromising decision-making processes rather than targeting IT infrastructure directly.\n\nShadow AI and exposure to synthetic data are growing concerns, as employees may use unsanctioned or unmonitored AI tools, models, and workflows for routine tasks. Agentic AI adoption without proper risk control, particularly third-party risk, is on the rise. Agentic AI significantly expands the threat surface, as systems can autonomously take actions, often bypassing traditional enterprise controls. This leads to blind spots when agents are connected to external systems with unvetted security vulnerabilities. Prompt injections, similar to third-party dependencies, pose another critical risk, as organizations may unknowingly allow malicious content to enter their systems.\n\nAs we approach \"Q-Day,\" when quantum computers become powerful enough to break current encryption standards, businesses face the potential interception, decryption, and compromise of nearly all global digital communications and financial transactions. To address these challenges, a formal framework for trust-enabled ROI in the age of AI is needed. This framework should prioritize maintaining data source integrity, with mechanisms such as identity and accountability to ensure humans are accountable for AI-driven decisions. Contextual authorization and constant behavioral validation are essential, as is maintaining human oversight throughout the AI lifecycle. Responsible AI approaches must consider the impact on the entire value chain in the ecosystem, ensuring value is delivered to all stakeholders.",
  "summary": "Helping security leaders take the right approach to securing their data in the AI era.",
  "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."
}