Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

On December 31, 2025, the DEA issued its fourth temporary extension of the COVID-era telemedicine flexibilities, keeping the current rules in place through December 31, 2026. For telehealth companies prescribing controlled substances, the extension was welcome news.

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.

Mac patch management is the process of identifying, testing, and deploying software updates across macOS endpoints and third-party applications to reduce the window of exposure before attackers can exploit known vulnerabilities. It's a foundational practice within any enterprise cybersecurity program, particularly as Mac adoption in corporate environments continues to grow.

PCI DSS controls are no longer just a compliance checkbox — they’re a mandatory security baseline that stands between your customers’ card data and sophisticated cybercriminals who are faster, smarter, and better-funded than ever before. According to the Nilson Report, global card fraud losses exceeded $33 billion in 2022 and are projected to surpass $38 billion by 2027.

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.

Everyone uses developer tools to get through the day. A JSONFormatter to inspect an API response, or a JWT decoder when you need to inspect a token quickly. In most engineering teams, these tools are treated as harmless productivity aids. In November 2025, researchers discovered that JSONFormatter and CodeBeautify had been storing everything users pasted into them via a save feature that generated shareable links with fully predictable URL structures. A simple crawler could retrieve all of them.