Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

DLP for AI: Protecting Sensitive Data in the Age of AI

Employees paste code into ChatGPT. They drop customer lists into Gemini for a quick summary. They upload a contract to an AI note-taker before a meeting starts. None of it feels risky in the moment. But all of it can walk sensitive data straight out of your organization. AI DLP exists to close that gap.

The EU AI Act Is Here. Is Your AI Environment Ready?

AI has moved from experimentation to operational reality. Last year saw a 50% rise in access to AI for employees, with 88% of organizations using AI in at least one business function. Competitive pressure is accelerating AI adoption. While this creates opportunity, it also creates a new level of operational risk.

The MemcycoFM Show: Ep 27 - What Is Agentic Threat Intelligence?

In the recently published blog from Memcyco titled "What Is Agentic Threat Intelligence?", we discussed agentic threat intelligence as an emerging CTI model. Bounded agents support repetitive investigation work, such as collection, enrichment, prioritization, and evidence packaging, while analysts retain control over takedown and escalation decisions. Vendor briefings are full of “agentic AI” right now. Most of them describe the same thing: faster dashboards and smarter alerts. That is not agentic threat intelligence.

TITAN AI Demo Series: How MAX Managed Questionnaires Eliminates Your Assessment Backlog

SecurityScorecard's MAX Managed Questionnaires handles your entire vendor questionnaire process end-to-end, design, outreach, response collection, and expert analysis — with no additional headcount required. In this installment of SecurityScorecard's Demo Tuesday series, see MAX Managed Questionnaires in action and what your security program looks like when your team is free to focus on risk strategy instead of assessment admin.

7 AI Governance Tools for Shadow AI Detection

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.

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

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).

JADEPUFFER: How an Agentic Ransomware Attack Unfolded

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.

Secure Enterprise AI Innovation with Cato AI Security

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.

Agentic AI Governance Requires a New Enforcement Model

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.