Wisecube AI

06/01/2026

Large language models are now embedded in tutoring apps, educational platforms, and healthcare chatbots that children use every day. The safety guardrails on most of those models were not designed with children in mind. Standard LLM safety evaluations test for harmful output in general adult context...

05/26/2026

05/25/2026

How to Use John Snow Labs' LangTest to Detecting and Evaluating Sycophancy Bias in AI and LLMs - read the article

05/21/2026

"A single do-everything model in healthcare is a black box."

That's how Tal Amitay, VP of Engineering at Brook Health, describes monolithic healthcare AI systems.

Brook took a different approach.

Instead of one model handling everything, they built a multi-agent architecture where translation, risk scoring, behavioral coaching, and escalation workflows operate independently — with separate guardrails and evaluation layers.

Because in healthcare, one failure should not compromise the entire system.

Their deployment included:
→ input guardrails for emergencies, self-harm, and adversarial prompts
→ output controls preventing unsafe or biased responses
→ governance defined before deployment, not after
→ human escalation paths with full clinical context transfer

The result is not slower innovation.
It's safer iteration at production scale.

Full Brook Health case study and webinar: https://pacific.ai/responsible-llm-deployment-in-practice-at-brook-health/

05/19/2026

Most AI governance work still starts the same way: opening 20 browser tabs and trying to figure out which policies your organisation actually needs.

EU AI Act.
NIST.
ISO.
US regulations.
Internal governance requirements.

Pacific AI’s 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗣𝗼𝗹𝗶𝗰𝘆 𝗦𝘂𝗶𝘁𝗲 gives teams 250+ ready-to-use policies already mapped across major AI frameworks and regulations.

Instead of starting from scratch, you start from an operational baseline.

Explore the Policy Suite → https://pacific.ai/ai-policies/

05/04/2026

Research paper on multi-domain red teaming for medical LLMs evaluating safety, robustness, and fairness across clinical scenarios to uncover hidden risks beyond accuracy.

04/30/2026

Most teams think they’re doing AI governance. In reality, they have policies and documents, but not a working system behind them. The gap shows up when you try to answer simple questions: what AI systems are actually in production, who owns them, what risks are tracked, and what is being monitored on an ongoing basis.

That’s where governance usually breaks down. Not because teams ignore it, but because it never becomes operational.

We built a simple way to check this. Pacific AI’s 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗤𝘂𝗶𝘇 gives you a quick snapshot of your actual maturity, highlights the gaps, and shows what to fix first.

Take the quiz → https://pacific.ai/ai-governance-quiz/

04/21/2026

Applied Healthcare AI Summit 2026 | Day 1 | MedHELM and the Next Phase of Open Medical AI Evaluation

04/20/2026

Your AI governance only looks strong —
until someone asks for proof.

A regulator.
An auditor.
Your board.

And suddenly:
→ no clear risk registry
→ no audit trail
→ no ownership
→ no way to explain decisions

That’s the moment most teams realise:
they weren’t governing AI — they were managing it informally.

We built a simple way to test that.

Pacific AII’s 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗤𝘂𝗶𝘇 gives you a fast, honest snapshot of where you actually stand.

3 minutes.
10 questions.
Instant score + clear gaps.

If you’re responsible for AI at scale, this is the easiest way to pressure-test your setup.

Take the quiz → https://pacific.ai/ai-governance-quiz/