Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

This week bore witness to some interesting events and milestones as Anthropic announced the availability of Claude Fable 5, a descendant of their Mythos Preview model, and Microsoft published their largest Patch Tuesday in history with over 200 vulnerabilities. The two are not unrelated.

Many organizations invest heavily in backup solutions but still face a critical gap: the absence of a well-defined backup retention policy. Without a structured retention policy, backups may either be stored longer than necessary, driving up costs, or deleted prematurely, increasing compliance risks and limiting recovery options. In critical scenarios like ransomware attacks or system failures, organizations may find that their backups are incomplete, outdated, or unusable.

API security is the practice of protecting the interfaces that connect your applications, models, and data from unauthorized access, abuse, and data theft. In AI applications, APIs carry prompts, model responses, customer PII, and agent instructions, which makes them the single most exposed layer of your AI stack. Securing them requires authentication, rate limiting, encryption, and a layer most teams miss: protection of the sensitive data in every API call.

The CMMC ecosystem is poised to be very strict in a very short amount of time, which means a lot of organizations are quickly finding that they need to do a lot of work in short order. A significant area of concern is where MSPs fall into the spectrum of security. Managed Service Providers are a key part of how modern digital businesses operate, but they’re also distinct and separate from the businesses themselves.

Risk-based vulnerability management (RBVM) is a cybersecurity methodology that prioritizes vulnerabilities based on actual business risk rather than technical severity scores in isolation. RBVM combines vulnerability severity, exploitation likelihood, threat intelligence, and asset criticality to focus remediation on the exposures most likely to be weaponized against your specific environment.

AI agents today are becoming a part and parcel of everyday enterprise operations. They can access databases, trigger workflows, send emails, approve requests, and interact with business systems with very little human involvement. What started as AI assistants is now evolving into autonomous operators capable of making decisions and executing actions at machine speed.

Quick answer: Today’s supply chain disruptions stem from surging demand for components, especially server CPUs feeding the AI build-out, rather than the pandemic-era shutdowns of 2020 and 2021. Companies can protect themselves by diversifying suppliers, locking in pricing terms early, holding strategic inventory, and investing in real-time visibility tools. Think of your supply chain like the plumbing in an old building. When everything flows, you never think about it.

Across enterprises, a familiar pattern is emerging. A business unit identifies an AI tool with a clear upside in productivity or revenue. Their proposal moves into procurement. Security raises concerns, and the legal team asks new questions about the tool. Compliance starts hesitating and the momentum slows. Finally, the project stalls. This friction is not due to resistance to innovation. It reflects a deeper structural issue: Most enterprise governance models were not designed for AI.