When Anthropic announced Claude Code Security on February 20th—a tool that scans codebases for vulnerabilities and suggests patches for human review—the reaction from markets was swift and brutal. Major cybersecurity names watched their stock prices fall by double digits within days. The implied thesis behind the selling: AI can now do what these companies do, so why pay for them? It's a compelling fear and an inaccurate conclusion at the same time. The DLP space is a clear example of why.

In 2026, the digital landscape has shifted from a world of "AI assistants" to one of autonomous operators. For managed service providers (MSPs), this evolution marks the end of the traditional "land and expand" human services playbook and the beginning of a high-speed era of machine-on-machine warfare.

Your CISO just got word that engineering is deploying AI agents into production Kubernetes clusters next quarter. Not chatbots—autonomous agents that generate and execute code, call external APIs through MCP tool runtimes, access internal databases, and make decisions without human review. The question lands on your security team: “How are we securing these?”

The University of Hawaii Cancer Center is the only National Cancer Institute-designated cancer center in Hawaii. Located in Honolulu, the center employs over 300 faculty and staff conducting critical epidemiological research studying cancer risks across diverse populations. In August 2025, the Cancer Center fell victim to a ransomware attack that exposed Social Security numbers of up to 1.15 million people.

Data governance best practices give organizations the documented policies, assigned ownership, and enforceable controls that auditors require. Without governance, compliance gaps emerge across access controls, retention enforcement, and audit evidence, creating exposure under GDPR, HIPAA, and SOX. Closing those gaps requires classification, accountability, continuous monitoring, and tooling that connects policies to evidence.

‍ ‍Cyber risk management plays a foundational role in enabling business resilience. As organizations today rely more heavily on digital infrastructure than ever before, the world's cyber threats have direct implications for operational continuity and revenue stability. The ability to manage these risks proactively, therefore, determines how well a company can absorb disruption and maintain performance under pressure.

Your security stack was built for workloads that follow predictable code paths. AI agents don’t. They interpret prompts, generate code on the fly, invoke tools dynamically, and escalate privileges in ways no developer anticipated — all as part of normal operation. The signals that indicate a compromise in a traditional container are indistinguishable from an AI agent doing its job. And most detection tools can’t tell the difference. This isn’t a theoretical gap.

PAM solutions in 2026 must cover non-human identities, enforce zero standing privilege, and deploy in days rather than quarters. Legacy vault-centric tools leave standing accounts in place between rotations, giving attackers persistent targets across service accounts and machine workloads. Evaluating modern PAM requires testing JIT access depth, AD/Entra ID integration, and real-world deployment timelines against your hybrid environment.

AI tools are everywhere. Value isn’t. Most organizations already use AI—chatbots answer questions, assistants summarize documents, and agents kick off workflows. And yet, day-to-day work often feels the same, with people still digging through folders and teams still double-checking decisions. AI exists, but the returns vary widely. The problem isn't with AI. It's the way the work is set up. Work is fragmented across tools, systems, and formats that were never designed to work together.