Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

teramind

7 AI Governance Tools for Shadow AI Detection

Jul 13, 2026 By Teramind In Teramind
AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI — often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.
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aikido

How to maintain code quality standards with AI code and vibe coding

Jul 13, 2026 By Berg Severens In Aikido
It’s amazing how non-developers have recently been empowered to create their own apps that can even generate revenue. We’ve recently seen progress across the AI development field, from AI being successful in “greenfield code” (apps built from scratch) towards “brownfield code” (larger scale existing applications).
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outpost 24

JADEPUFFER: How an Agentic Ransomware Attack Unfolded

Jul 13, 2026 By Martin Jartelius In Outpost 24
In early July 2026, researchers at Sysdig published an analysis of what they assess to be the first documented case of agentic ransomware. The threat actor, which Sysdig calls JADEPUFFER, launched an extortion attack driven end to end by a large language model (LLM) rather than a conventional human-operated toolkit.
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Secure Enterprise AI Innovation with Cato AI Security

Jul 13, 2026 By Cato Networks In Cato Networks
Enterprise AI is spreading fast across employees, applications, and agents. Security teams need a way to enable AI adoption without losing visibility, control, or governance. In this demo, see how Cato helps organizations secure AI across three fronts: · AI employees use, including sanctioned and unsanctioned AI tools· AI applications teams build, including LLM apps connected to enterprise data· Agentic AI, where agents can access tools, data, and workflows.
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cyberhaven

Agentic AI Governance Requires a New Enforcement Model

Jul 13, 2026 By Cyberhaven In Cyberhaven
AI has swiftly shifted from a browser-based chat interface to an autonomous actor operating within enterprise environments. Agents run locally on endpoints, inherit employee permissions, access sensitive data in bulk, and execute multi-step workflows with no human approving each step. That shift fundamentally changes the enforcement surface. The governance programs most organizations have built were designed for a different model: one user, one prompt, one decision.
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upguard

Data leakage risks with DBHub MCP servers

Jul 13, 2026 By Greg Pollock In UpGuard
Organizations keep their databases behind firewalls for a reason: the data inside is the data they can least afford to lose. A new class of AI middleware–Model Context Protocol (MCP) servers–exists specifically to reach into those protected systems on an AI model's behalf. One of them, DBHub, connects directly to SQL databases.
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What Server Do You Need to Run an AI Language Model?

What Server Do You Need to Run an AI Language Model?

Jul 13, 2026 By SecuritySenses In SecuritySenses
Running your own AI model sounds exciting, but one question appears almost immediately: what kind of server do you actually need? A small language model can run on a personal workstation, while a large 70B parameter model may require enterprise-grade GPUs and expensive infrastructure. Choosing the wrong hardware can lead to wasted money, unnecessary complexity, or disappointing performance.
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kovrr

What AI Governance Tools Exist in the Market Today

Jul 12, 2026 By Kovrr In Kovrr
‍AI governance tools are software platforms designed to help organizations manage AI risks, ensure regulatory compliance, and enforce responsible AI use across the machine learning lifecycle. The market has expanded rapidly, and in 2026 it includes tools spanning compliance automation, model observability, data governance, infrastructure security, and integrated risk quantification.
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The Safety Problem Nobody Warns You About When You Start Training a Language Model

The Safety Problem Nobody Warns You About When You Start Training a Language Model

Jul 12, 2026 By SecuritySenses In SecuritySenses
There's a version of the LLM safety conversation that stays comfortably abstract - AI alignment, existential risk, theoretical failure modes that matter at a scale most organizations will never reach. That conversation is important, but it's not the one most product and technology leaders need to be having right now. The one they need to be having is more immediate and considerably more practical: how the specific decisions made during llm training services directly shape whether the model you deploy is one your organization can actually stand behind.
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