Cloud environments create a flood of security signals, often reaching tens of thousands per day depending on the organization’s size. Security engineers and analysts spend a disproportionate share of their time triaging these signals instead of acting on legitimate threats. But the time-intensive parts of that work, such as identifying related signals and building a timeline, can be handled systematically, leaving teams free to focus on what actually requires human judgment.

I was on ABC News recently discussing why banks are on alert as new AI systems like Anthropic’s Claude Mythos raise cybersecurity concerns. What struck me most is how quickly the conversation has shifted. This is no longer a hypothetical risk or something we are planning for in the future. Financial institutions and regulators are reacting in real time to what AI is already capable of doing. From my perspective, we are still underestimating how fast this is moving.

Complexity has become a defining security challenge as organizations expand across hybrid and multi-cloud environments. In fact, 52% of surveyed organizations ranked multi/hybrid cloud complexity among their top three infrastructure concerns.1 This complexity creates fragmented visibility across cloud providers, workloads, and Kubernetes environments — gaps that adversaries increasingly exploit to move undetected.

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As AI adoption accelerates on Google Cloud, the challenge for most teams today is no longer just building AI-powered applications. It’s also managing the full AI stack from end to end, including data pipelines, infrastructure, release process, and security operations. Many teams are monitoring these layers with different tools, creating complexity, fragmenting visibility, and slowing decisions on what to do next.

For decades, passwords have been the standard method for protecting access to systems and accounts. However, passwords can be compromised or stolen via tactics such as brute-force attacks, phishing attacks, and infostealer malware. The shift to multi-factor authentication (MFA) added another layer of security by requiring additional authentication to verify the user’s identity – some combination of something you know, own, or (in the case of biometrics) are.