Microsoft’s February Patch Tuesday addressed six actively exploited zero-days, with additional attention on command injection risk impacting developer copilots and tooling. New research also highlights AI recommendation poisoning that manipulates memory and personalization, while identity governance and AI-themed social engineering remain central as AI systems expand into both enterprise platforms and physical-world deployments.
What happens when AI agents gain autonomy, access to sensitive data, and the ability to trade exploits? Moltbot and Molt Road offer a glimpse into a future where cybercrime operates without human hands.
Security leaders emphasized that AI agents can behave like insider threats if not tightly governed, while prompt injection remains a durable risk for tool-using systems. The week’s outlook reporting reinforces that AI-driven phishing and faster exploitation cycles will pressure identity, supply chain, and resilience controls in 2026.
Vector databases power RAG but also expose new leak paths. This playbook shows how embedding leakage and query-driven exfiltration happen, and how to stop them with access controls, input scrubbing, monitoring, and adversarial testing.
AI Security, Incident Response, Digital Forensics, Model Integrity, Cloud Security, MITRE ATLAS, NIST SP 800-61, Vertex AI, Threat Intelligence, Cyber Defense Playbook
Today’s briefing highlights identity risks from agentic AI, advanced endpoint DLP tailored for AI data flows, and Microsoft’s warning that AI-enabled threats are accelerating rapidly in the wild.
AI-powered forgeries are infiltrating contract and payment workflows. This playbook explains how synthetic contracts and invoices are created, highlights real incidents, and outlines practical defenses — from certificate-backed signatures and sandboxing to dual-control verification.
AI voice-cloning lets criminals mimic familiar voices to commit fraud. This playbook explains real-world cases, threat mechanics, and countermeasures like multi-channel verification, liveness detection, and awareness training.
Stealth bias injection hides subtle, high-impact model bias inside retraining or feedback loops. This playbook explains how these attacks work, realistic scenarios, and practical defenses: provenance controls, subgroup testing, adversarial drills, and gated retraining.
Attackers can clone ML models or extract memorized data through API queries. This playbook details mechanisms, real-world research, and defenses such as output minimization, DP, and active monitoring.