A concise, fact-based update for security and risk professionals. Core security news first, followed by broader AI risk and governance context.

🔐 Core Security Intelligence

1) Agentic AI security enters new phase with identity-centric control models

What’s new:
Research from Delinea (2025 AI in Identity Security Report) shows that 66 % of organizations actively deploy agentic AI systems and 56 % encounter “shadow AI” incidents at least monthly. The article outlines three key strategies for securing these systems: AI-to-AI credential brokering, visual digital identity mapping, and enhanced privileged access management.
Source: Help Net Security

Why it matters:
As autonomous agents increasingly operate across cloud, infrastructure, and application layers, traditional identity and access models become inadequate. Without identity-centric governance, agentic systems present massive privilege escalation and audit-blind risk surfaces.

Defenses:

• Inventory and classify AI identities immediately. Discover all agentic-AI instances across the enterprise, categorize them by privilege and business impact, then enforce least-privilege and just-in-time access for each.
• Implement AI-to-AI credential brokering. Ensure that every agent-agent and agent-system interaction is authenticated and authorized, reducing the risk of rogue automation or lateral chain-access.
• Visualize agent identity relationships and monitor behavior. Create dynamic maps of human, agent, and system identities and overlay behavioral analytics to detect anomalies, misuse, or escalation events.

Expert Insight:
In the agentic AI era, identity becomes the perimeter—agents act like users with machine speed and scale. Treat them accordingly. Security strategies must shift from systems-centric to identity-centric, and defenders must make agent identities as visible and governed as human ones. Without this, an overlooked AI identity could be the entry point for major compromise.

2) New endpoint DLP platform built for AI-era data leakage

What’s new:
MIND announced an upgrade to its endpoint data loss prevention (DLP) platform. The enhancements include AI-driven classification, full file-lineage tracking, native application coverage (including GenAI apps), and peripheral controls like USB and device management. The vendor claims this offers a more seamless and automated protection layer tailored for the AI era.
Source: PR Newswire

Why it matters:
With AI tools increasingly used for research, development, or even casual productivity tasks, more sensitive data is handled via endpoints and often outside traditional controls. A modern DLP model must evolve to capture AI-tool interactions, model ingestion risks, and adaptive exfiltration vectors.

Defenses:

• Deploy advanced endpoint DLP immediately where AI tools are used. Focus on classification policies tuned for generative-AI file interaction, model training data, and unstructured content transfer.
• Monitor full data-lineage from device to cloud to model. Track sensitive files as they move, get ingested by AI tools, or get shared externally, and alert on anomalous flows.
• Extend controls to peripherals and external devices. Block or monitor USB/device access during critical operations (e.g., model training or sensitive dataset transfers) and enforce encryption, policy review, and audit logs.

Expert Insight:
Data leakage in the AI era isn’t just about uploading a file, it’s about ingesting it into a model, exposing model weights, or sharing derived insights. Protecting endpoints means protecting the full data-AI lifecycle. The vendor upgrades show that defenders are catching up, but early deployment and policy integration are key to success.

🌐 Extended Reading / Broader AI Risk & Governance

3) Microsoft reports accelerated AI-driven threats and calls for hybrid defense models

What’s new:
In its 2025 Digital Defense Report, Microsoft found adversaries—criminal and state-sponsored—are using AI for social engineering, vulnerability discovery, and lateral movement. The firm argues that legacy perimeters are no longer sufficient and emphasizes resilience, supply-chain oversight, and real-time intelligence sharing.
Source: Industrial Cyber

Why it matters:
The threat landscape is shifting rapidly: attackers are adopting AI as a force multiplier. Defenders who rely on static controls or signature-based tools will fall behind. The report places responsibility on executives to treat cyber risk as business risk—and to fund cross-domain defense.

⚠️ Updates / Follow-ups

No significant updates today to previously covered stories.

Summary Table

Categories: Cybersecurity News

Tags: Agentic AI, AI Security, Endpoint DLP, Threat Intelligence