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Law firms are prime targets for cyberattacks. They handle sensitive client data, confidential case strategies, and privileged communications-information that's highly valuable to hackers and malicious actors. As digital threats grow more sophisticated, traditional security measures like passwords and firewalls are no longer enough on their own.

AI-generated code is no longer experimental. It is actively running in production environments across SaaS platforms, fintech systems, marketplaces, internal tools, and customer-facing applications. From AI copilots assisting developers to autonomous agents opening pull requests, the volume of machine-generated code entering production has increased dramatically. This shift has created a new operational challenge: how do you reliably monitor AI-generated code once it is live?

By 2026, container image security will no longer be evaluated in isolation. For most organizations, the image layer has become one of the primary sources of security debt, quietly accumulating vulnerabilities that multiply across services, clusters, and environments. What has changed is not just the volume of vulnerabilities, but the cost of managing them. Faster release cycles, shorter maintenance windows, and tighter compliance expectations have pushed teams to reconsider whether traditional scanning-and-patching workflows are sustainable at scale.