In just a few years, Anthropic transformed from a safety-focused AI startup into one of the most consequential companies in cybersecurity. But, aside from systems generally considered secure today, what does this mean for systems that have routinely proven to be insecure? This is where Microsoft comes into play.
Boards are starting to ask the right question about AI risk. Unfortunately, many organizations still don’t have a credible answer.
Most of the conversation around AI in cybersecurity focuses on how attacks are getting faster and more sophisticated. That is true, but it misses a more immediate issue. Many security teams are still operating in ways that assume a much slower threat environment.
As data is continuously collected and acted upon, transparency becomes the mechanism through which organizations demonstrate responsible stewardship. In a world where technology acts on behalf of the consumer, trust becomes the ultimate differentiator.
Cyber risk has become a barometer for corporate resilience and trust. As the landscape accelerates, boards are expanding how they engage with performance, talent, and technical insight to keep pace with rising expectations. In today’s environment, traditional rhythms are giving way to more dynamic approaches that reflect the speed of change.
The idea of continuously verifying access rather than assuming trust is more relevant than ever, but the challenge is that many organizations implemented Zero Trust as a fixed framework in environments that have since become highly dynamic.
Peacetime — before an attack occurs — is when to plan for disaster recovery and operational resilience. This is where asset dependency mapping will play a critical role in determining an organization’s ability to recover from an attack quickly and fully and emerge even stronger.
Security leaders often assume patching failures stem from technical limitations. In reality, many of the most disruptive patching delays originate from coordination breakdowns across teams, tools, and timelines.
File-based malware has long been among the most effective attack vectors employed by threat actors worldwide. While AI-powered detection technologies are coming to market to help address these growing risks, their outputs should be complemented by deterministic controls and human oversight, particularly in high-consequence environments.
Wall Street is now demanding evidence of product uptake and pathways to profitability—and Microsoft is stumbling. The company’s latest earnings report led to a large drop in share prices, as investors and analysts raised concerns about its massive spending on AI infrastructure without the kinds of tangible returns that a really valuable product should demonstrate.










