Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams are drowning in findings, not because scanners miss things, but because nothing confirms which ones an attacker could actually reach. Seemplicity AI Analysts run the investigation themselves, checking runtime configuration, network reachability, and exploit conditions for each finding, and re-rank your backlog by confirmed exploitability. What rises to the top is backed by evidence. What drops down has been checked and reasoned out.

Recently, I had the pleasure of presenting an AI governance-focused webinar with my colleague Neil Jones at Egnyte. In the session, we discussed many ways to improve AI governance, and you can watch and share the complete session replay here. During the session, we discussed the importance of respo nsible AI usage policies. However, my experience is that many organisations struggle to create policies aligned with their business requirements and the technological solutions that they use.

It only takes one accidental file share, one rogue USB drive, or one compromised account to turn your company’s sensitive data into a costly headline. That’s where DLP monitoring steps in. Think of it as a smart, real-time safety net that tracks, detects, and blocks unauthorized data transfers before the damage is done. But what does effective monitoring look like in practice, and how do you deploy it without bottlenecking your team’s daily workflow?

There’s a category of security risk that most organizations aren’t ready for. It doesn’t live in your code repository, your CI pipeline, or your developer laptops. It lives in your runtime, in the autonomous AI agents already running in your environment, extending their own capabilities, and making decisions that no human explicitly approved. This is the challenge JFrog set out to address with our integration with NanoCo AI and their open-source agent framework, NanoClaw.

As organizations aggressively shift from static Large Language Model (LLM) chatbots to fully dynamic, autonomous AI agents (e.g. systems designed to plan workflows, call APIs, write runtime code, and modify enterprise databases), traditional compliance and governance frameworks are hitting a breaking point. A landmark press release from Gartner highlights a critical systemic risk: treating AI agent governance as a monolithic, one-size-fits-all policy guarantees project failure.

To detect and investigate threats, security teams need to collect telemetry data from identity providers, cloud platforms, web application firewalls, and endpoints. But these diverse sources describe the same tactics, techniques, and procedures (TTPs) differently according to their own vendor-specific language. For example, a failed Windows logon appears as an event ID, while an Okta account lockout appears as an identity event.

Sales enablement is the process of equipping sales teams with the content, tools, training, and information they need to engage buyers effectively and close deals. Most organizations scope it to pitch decks, competitive battlecards, CRM workflows, and onboarding programs — and in doing so, they overlook a component that quietly costs them deals: the security review.

On June 24, 2026, demonstrating the power of public-private collaboration, Europol and the Microsoft Digital Crimes Unit, alongside our team and other global partners, executed a coordinated disruption as part of Operation Endgame, impacting two of the most prolific commodity malware families on Windows: the Amadey loader/botnet and the StealC information stealer.