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Rethinking AI Governance with Lisa Pent #Cloudera #Shorts #AIGovernance #TechRisk

New AI tools are coming out faster than ever, and your employees are working hard to stay at the cutting edge of new technology. Where does governance come into play? Explore how to govern AI without slowing down innovation in the latest episode of The AI Forecast, sponsored by Cloudera.

The Reason Behind Stalled AI Projects

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

How to Proxy Every AI Traffic Pattern Through One Gateway

Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.

From Prompt to Report: Artifacts in Databox

Ask Genie a question. Get a report back, ready to share, with real data already in it. This is Genie Artifacts, Databox’s AI analyst turning a prompt into a finished document. Watch it build a report from scratch, convert that report into a slide deck with a single follow-up prompt, then get shared as a public link anyone can open, no Databox login needed. What you’ll see: Genie pulls from 130+ connected data sources, so every report and deck is built on numbers your team already trusts. No manual formatting, no rebuilding for a different format, no dashboard for the recipient to log into.

AI to Write Rules, or AI to Make Decisions?

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

Announcing Kong AI Gateway 2.0: Built for the Pace of Agentic AI

We have big news for platform and AI infra teams: *Kong AI Gateway 2.0 is available today in private beta*. It runs on its own dedicated runtime, ships on its own release cadence, and carries a completely reimagined user experience designed around the way teams actually build with AI: models, MCP servers, and agents as first-class citizens, not plugins bolted onto an API gateway.

Enterprise AI Testing Checklist: From Pre-Deployment Evaluation to Live Runtime Guardrails

While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.

Why It Matters: Data and AI Literacy Is Now a Business-Critical Skill

One thing has become increasingly clear to me: the businesses that thrive in the AI era won't be the ones with the most data, they'll be the ones where every employee knows how to use it. The World Economic Forum's 2025 Future of Jobs Report names analytical thinking as the top core skill companies need today, and a 2024 Gartner survey found poor data literacy to be one of the top five obstacles to analytics success.

How Enterprise Teams Are Validating AI-Generated Code at Scale | Perforce 2026

Your testing strategy was built for a world before AI wrote code. That world is gone. AI is now generating code, reviewing pull requests, writing tests, and analyzing defects, faster than any team can validate it manually. In this session, Perforce CTO leaders Anjali Arora and Rod Cope sit down with VP of Product Steven Feloney to break down why traditional test automation can't keep pace, and what comes next.