Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The proliferation of credentials outside centralized visibility and control is known as “credential sprawl,” and attackers are eager to take advantage of it. Unfortunately, credential management is a broad problem that only grows in complexity as organizations add new tools, employees, and partners.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo 92% of security leaders say something is actively reducing their trust in AI within the SOC. These aren’t skeptics, they’re people who have already adopted AI and believe in its ability to enhance security operations. We know from the 2026 AI SOC Leadership Report that AI is already widely adopted in the SOC, with 94% of organizations using it in some capacity.

Agentic AI is forcing a hard question on every security leader: when your SOC is full of autonomous “doers” instead of just dashboards and scripts, is that your new best friend or a brand‑new risk surface you barely understand? The honest answer is both, and the way you design, govern, and deploy these systems will decide which side wins.

BOSTON, May 7, 2026 — Snyk, the AI security company, today announced it is leveraging Anthropic's Claude models to advance software security in an era of AI-powered development. Starting today, Snyk has integrated Claude into the Snyk AI Security Platform — powering automated vulnerability discovery, prioritization, and developer-ready fixes across code, dependencies, containers, and AI-generated artifacts. The threat driving that integration is real and accelerating.

A recent American Banker article, “Knock on wood: Are banks doing enough to cope with Mythos?” raises a timely and uncomfortable question about advanced AI models like Anthropic’s Claude Mythos. As highlighted in the article, INETCO CEO Bijan Sanii points out a critical truth: The conversation is being fueled by the emergence of AI technology capable of identifying software vulnerabilities at a speed and scale that was previously unimaginable.

Every few weeks, a new story surfaces: an AI agent deletes a production database, an autonomous coding tool racks up a five-figure cloud bill, or a chatbot exfiltrates internal documents through a prompt injection attack. The reaction is predictable. “AI is dangerous.” “LLMs can’t be trusted.” “We need better guardrails on the model.” But if you look at the root cause of these incidents, the model is rarely the problem. The system around it is.

As 2026 rolls on, our capacity to prompt ourselves silly appears to be limitless. We’ve already seen the financial, legal, and reputational damage to Deloitte as they partly refunded the Australian government for a 237-page audit report containing LLM-generated hallucinations like fabricated academic references, fake footnotes, and a false quote attributed to a judge.

Your risk committee meets Thursday. The agenda has a new item: AI agent risk posture. You open the register. The fraud detection agent shipped in March is on it. So is the customer service agent. Neither row is useful — “likelihood: medium, impact: high, control: service account scoped via IAM.” Three months ago that was approximately right. Last week the platform team added two MCP connections, the model was upgraded, and the agent now touches data classes the entry never anticipated.

Editing IAM policies cannot fix the most common architectural mistake in shipping AI agents on Kubernetes. It happens in thirty seconds: a platform engineer reuses an existing ServiceAccount with an IRSA annotation for Bedrock access because creating a new one takes thirty minutes plus a Terraform pull request. The new agent ships under the existing identity.

The residency evidence GDPR and the EU AI Act now expect lives in the runtime trajectory of every AI agent execution, not in the deployment configuration. Your residency compliance dashboard — every workload in eu-west-3, sovereign cloud configured, SCCs signed — cannot produce it. Your AI agent’s last thousand inferences crossed an external border, on average, eight times each. The translation API routed through us-east-1 when the EU endpoint hit capacity.