🔎 Frontier model smarts alone don't run production AI. It works only when paired with a retrieval layer that pulls enterprise data accurately, with permissions and audit. TL;DR: Elastic and OpenAI expand their partnership, turning Elasticsearch into a permission-aware persistent memory layer to boost agents, observability, and security at once. Title: Elastic and OpenAI collaborate to bring frontier intelligence to unstructured enterprise data URL: Key points 🧠 Elasticsearch as a persistent memory layer (lexical + vector + reranking + access control) 🎯 Precomputed Knowledge Indicators cut input tokens up to 75%, lift accuracy 60%→92% 🔒 Internal tests show 0.89 recall with complete cross-user data protection 🛡 Attack Discovery correlates alerts into attack chains, mapped to MITRE ATT&CK ⚡ Visa cut triage from 10-20 min to seconds; Airtel up to 40% faster analysis, 30% faster investigations 🔌 Agent Builder exposes skills via MCP; migrates Splunk/QRadar rules A pragmatic design: intelligence + retrieval + governance together is what makes production AI real. #Elasticsearch# #OpenAI#

When users accidentally drop PII into a prompt, don't block the whole request. Watch how to use Sensitive Data Protection to partially redact prompts on the fly, keeping your agents running without leaking data →

Every time you pay someone from your main wallet, you aren't just sending a transaction, you're handing over a statement of your financial history. Imagine if every store clerk, contractor, or landlord you ever transacted with could instantly see your total net worth and every asset you hold. That’s the reality of a public ledger. Web3 cannot scale to global enterprise adoption until we realize that data protection isn't an option; it's a structural necessity.

Logging alone won't prevent incidents. Meet runtime governance that blocks problematic requests before they reach the LLM 🛡️ Title: LangSmith LLM Gateway: runtime governance built into the agent lifecycle URL: 🛡️ Overview A runtime governance layer that sits between agents and their model providers. As an enforcement point inside the LangSmith platform, it aims to stop problems at the source rather than just logging them after the fact. ❓ Challenges Solved Observability (logging) alone can't prevent problems. Logging an incident after it happens is too late — problematic requests should be blocked before they reach external LLM providers. 💡 How It Works ・Spend controls: hard caps at org, workspace, user, or API-key level; returns 402 when exceeded ・Cost visibility: real-time spend tracking across org units ・Data protection: auto-redaction of PII and secrets before they reach the model ・Trace integration: gateway-proxied calls appear in the same workspace ・Audit logging and layered policies Setup is minimal: point base_url at the Gateway, store provider keys in workspace secrets, and define policies in the LangSmith UI. 🌍 Use Cases ・Preventing runaway agent spend from retry loops ・Stopping sensitive data (SSNs, PII) from leaking into provider logs ・Establishing org-wide cost governance and compliance auditing #LLMOps# #AIGovernance#

The Netherlands has blocked a U.S. company from buying Solvinity, a Dutch cloud provider connected to DigiD, the digital ID system millions of people in the country use every day. DigiD is used for taxes, healthcare, pensions, and local government services, so it plays a major role in daily life in the Netherlands. The U.S. company, Kyndryl, wanted to buy Solvinity, but the Dutch government stopped the deal over concerns about security and control of sensitive citizen data. Officials worried that U.S. ownership of infrastructure linked to DigiD could place Dutch data under U.S. laws like the CLOUD Act, allowing American authorities to request access. Its one of the first times the Netherlands has blocked an American tech acquisition over national digital security and data protection concerns.