CJ Combs explains how cross-functional data governance can help enterprises uncover hidden data risks, establish accountability, and build processes to remediate them.
governance
Experts including forward-deployed engineers (FDEs) are deemed key factor in accelerating outcomes, reducing risk, and ensuring operational impact of AI.
Customers increasingly wonder whether communications, statistics, and research were reviewed by people, driving the need for published AI policies to build trust.
‘Project HydraFusion’ evaluates each request and chooses the least complex workflow required, using additional model calls only when it anticipates a better result.
Product provides budgets, guardrails, and lock triggers to actions taken by AI agents. Can organizations define clear limits and policies to make use of those controls?
New tools aimed at enforcing cost discipline include an AI pricing calculator, detection of spending anomalies, and spending caps for individual AI projects.
New features enhance positioning of Teams as a platform to support humans, agents working together to accelerate GitHub-powered dev projects.
Gmail, Calendar, and Drive users can now access data and perform tasks directly from their Claude conversations, accelerating integration of AI tools and productivity tools.
Microsoft’s Copilot Acceleration Team provides a rich, open-source repository of pre-written skills for specific tasks. Users need to exercise caution because they’re not supported product features.
Skill Recorder creates a foundation for AI to understand workflows, recommend improvements, and ultimately automate workflows, for on-demand or scheduled use cases.
Identity management, data loss prevention tools work together to ensure every AI agent action can be traced to the entity that triggered it for accountability.
Let agents discover and reason through the work, move repeatable parts into deterministic automation, and then use the agent as a supervisor and exception handler.
Even with strong partner support, AI operating in silos will yield disconnected workstreams due to the lack of a single point of governance and accountability.
Developers now have three harnesses to choose from, selecting the best option and model for the required tasks while also factoring in cost considerations.
Certification paths combined with AI platform-wide bet on governance steer customers to adopt existing frameworks and avoid creating technical debt.
Project Perception scales vulnerability discovery for today’s AI-powered threats. Initially available in Defender, Perception will extend across the company’s security lineup.
As defined by Databricks, a context decides what an agent sees, in what form, at what fidelity, and at what moment to ensure it performs its intended tasks.
Upgrades include automatically created documents and files, as well as interactive reports. Approvals and other repeatable work can be handled through automation.
New security features include agent inventory, threat detection for core AI tools, and automated investigations that isolate legitimate threats at scale.
Purview, Defender, and Graph API are all being enhanced to bring agents running on end-user devices under unified governance ‘control plane.’