AI data governance is the structured framework that ensures sensitive data remains protected when artificial intelligence systems are used. Traditional data governance focuses on data at rest. It manages databases, access controls, storage policies, and compliance documentation. AI fundamentally changes the environment, and hence, understanding AI data and privacy is crucial. When organizations use large language models, AI agents, or retrieval-based systems, data flows dynamically.

Since 1Password began, we have built security into the places where work actually happens. Security is not treated as an overlay or a separate workflow, we build directly into the browser, command lines, developer tools, and IDEs, where decisions are made and actions take place. We believe that if you want to improve security outcomes, you build where the work happens, making the secure path the simplest one.

The next major security problem enterprises will face won’t originate in the cloud. It will emerge on endpoints, where agentic AI is already operating with autonomy, authority, and access to sensitive data.

In my experience working inside banks, identity security can be like plumbing: when it’s working, no one wants to talk about it. When there’s an incident, an audit, or a regulator—suddenly everyone wants to understand how it works. Artificial intelligence (AI) brings the same “no one cares until everyone does” energy, but with face-melting velocity. Today, AI is embedded across large parts of the financial services industry, and it has been around for more than 25 years.

CVE-2026-22769 is a hardcoded credential vulnerability affecting Dell RecoverPoint for VMs, a disaster recovery orchestration platform used to manage replication and failover of virtualized workloads. The issue stems from static authentication credentials embedded within a product component. Because these credentials are not uniquely generated per deployment and cannot be changed by administrators, they introduce a structural authentication weakness.

Why do most Kubernetes security tools fail teams in practice? Because they treat deployment and security as separate problems. A true Kubernetes security deployment service embeds scanning, policy enforcement, and runtime monitoring directly into the deployment flow — so risky workloads never reach production in the first place. Why isn’t shift-left security enough on its own?

The thing about blanket approaches is that they rarely work or scale. The same holds true for third-party cyber risk management. Treating every provider, stakeholder, or partner with the same intensity is neither productive nor cost-effective. While defaulting to treating every vendor at the same risk level is common, it is not a resilient security strategy.

Non-human identities are the fastest-growing and least-governed identity population in most environments. Service accounts, API keys, and AI agents run without MFA, without owners, and without expiration. Traditional identity and access management (IAM) wasn't built to manage them. Without governance for discovery, ownership, and lifecycle management, stale machine credentials become attacker footholds that persist for months.