Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For years, VPNs have been the standard for secure remote access. But as organizations embrace hybrid work, cloud applications, and distributed workforces, traditional VPN architectures are struggling to keep pace with today's security and operational demands. Legacy VPNs often grant broad network access, increasing the attack surface and creating challenges for IT teams tasked with securing users, applications, and data.

A lot of defense contractors are in the same spot right now. A solicitation lands, the DFARS language gets stricter, someone asks whether the company is “CMMC ready,” and the room gets quiet because nobody is fully sure what that means in operational terms. Usually, the first instinct is to gather policies, dust off the old SSP, and start checking controls in a spreadsheet. That's not enough anymore. CMMC doesn't reward paper maturity.

Backups are more common than you think. Every day, you probably rely on one without realizing it, whether it’s a coworker who covers your shift or that spare tire tucked in the bottom of your trunk for a flat. Backup and recovery plans apply to nearly everything in daily life. The same logic applies to your business, but the stakes are far higher. Data loss can happen in a heartbeat, and the companies that survive are the ones that planned ahead.

ShadowPad, a sophisticated modular malware, has emerged as a significant cybersecurity threat. Attributed initially to Chinese state-sponsored threat actors (APT41), this malware has evolved into a shared tool among various APTs. Its highly customizable nature allows attackers to adapt ShadowPad to specific targets, making it a versatile and persistent threat.

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.