Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Predicting a market disruption is difficult, but the vast rewards of being correct make it worthwhile. Unfortunately, prediction becomes tougher when marketing teams start labelling everything as a "market disruptor". Much like the stock market, if something is being sold to you as “the investment of a lifetime”, it almost certainly is not. Yet market disruptors do exist, and the organizations that identify them enjoy generational success.

Learn how validated IOCs reduce false positives, improve SOC efficiency and give analysts time back by delivering contextual, high-confidence threat intelligence. SOC analysts begin each shift facing a relentless stream of alerts driven by indicators of compromise (IOCs). Many of these alerts are false positives, some are outdated, and only a small fraction genuinely reflect malicious activity.

For years, the cybersecurity industry has suffered from a "data gravity" problem. Security teams are buried under billions of rows of telemetry, yet they remain starved for actionable insights. A Threat Intelligence Platform (TIP) is a centralized security system that collects, aggregates, and organizes data about known and emerging cyber threats. It serves as the vital connective tissue between raw telemetry and active defense.

Today’s threat landscape is more varied and chilling than ever: Sophisticated nation-state actors. Hyper-volumetric DDoS attacks. Deepfakes and fraudsters interviewing at your company. Even stealth attacks via trusted internal tools like Google Calendar, Dropbox, and GitHub.

The last 12 months have been the most challenging in Tines’ history. They’ve also been the most successful. We navigated macroeconomic headwinds and breakneck technological innovation. At the same time, global growth and scale demanded new operational discipline and relentless focus. But alongside those challenges came major milestones. We maintained a world-class 122% net revenue retention (NRR).

Mobile app risk rarely emerges from negligence. It emerges from fragmentation. In most enterprises, security is applied in stages: Each control works in isolation. None governs how risk evolves over time. Mobile applications are distributed, long-lived systems. Once deployed, they operate outside centralized infrastructure control, exposed to shifting SDK dependencies, evolving APIs, regulatory change, and adaptive adversaries. Security gaps rarely appear within a stage. They appear in the transitions.

Sending a work email to the wrong person – it’s something all of us have done at least once in our working lives. For some people, it’s a regular occurrence. But just how risky is it? Thinking back over your recent emails, you can probably pick out the ones that would have been worse to misdirect than others. In the best case it’s a non-issue or only slightly embarrassing.

There’s a moment every IT and security team recognises. You’ve done the hard work: rolled out SSO, tightened access policies, and moved sensitive tools behind stronger authentication. On paper, it looks like progress.

If you work in IT right now, your feed is probably split between AI hype, AI fear, and confused memes about both. Depending on who you ask, AI is either coming for your job, coming for everyone’s job, or going to “free you up to do more strategic work”—which somehow always looks like doing the same work, just faster, with fewer people. Some of that fear is legitimate.

Deterministic security tools, at this point, have become such a regular part of security that, for a long time, we weren’t questioning the alternatives. With AI becoming a core component of security with probabilistic models, it’s time to revisit determinism and get clear about what it’s needed for. Otherwise, why shouldn’t we just start replacing everything with AI?