Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The pressure to deliver applications quickly has created a complex software supply chain that is vulnerable to more threats than ever before. New regulations are shifting the liability to software developers, demanding auditable proof of security across the entire product lifecycle. Caught between velocity and complexity, the critical question is this: Can you truly vouch for the integrity, security, and compliance of every application that leaves your pipeline? What about after it’s deployed?

The rapid pace of AI innovation is driving new possibilities for every organization. Yet, for many, the journey from inception to reliable, production-ready AI applications is riddled with hidden challenges: proliferation of models, security blind spots, and a desperate need for consistent governance. You want to harness the power of AI, but not at the expense of control, security, or compliance.

Software supply chains are the attack vector for cybercriminals, and the challenge isn’t just finding vulnerabilities; it’s fixing them fast while ensuring security, compliance, and developer productivity. As supply chains grow in complexity, traditional tools aren’t enough; organizations need intelligent, autonomous assistance embedded directly into developer workflows.

(Nov 26, 2025) JFrog continues to track, provide research and document a second wave of the Shai-Hulud Software Supply Chain Attack. Following the initial campaign, threat actors have returned with more advanced tactics, compromising an additional 621 new malicious packages across leading public registries.

Within a Security Operations Centre (SOC), threat intelligence is indispensable. It provides the context analysts need to cut through noise, correlate indicators of compromise (IOCs), and prioritise alerts based on real-world risk. Without it, SOC teams would be overwhelmed, drowning in log data, chasing false positives, and reacting blindly to incidents rather than proactively mitigating them.

AI now sits inside customer support, finance, human resources and product development. That reach brings value, and it also exposes personal and sensitive data in new ways. The question is no longer whether to adopt AI. The question is how to adopt it responsibly, with AI data privacy built into the system rather than tacked on after a test run. This guide explains the core concepts, definitions and best practices you can use to design, ship and scale AI with privacy in mind.

Businesses everywhere are moving fast to adopt AI. Yet many initiatives are fragmented, siloed, difficult to scale, and lacking adequate governance. New research from Forrester Consulting, commissioned by Tines, surveyed more than 400 IT leaders in North America and Europe on the challenges of scaling AI and the role IT can play. The findings show that while governance, security, and cross-functional alignment are top priorities, they’re also some of the biggest barriers.

In 5-7 years, quantum computers will likely crack RSA and other currently used encryption methods. That’s not fear-mongering. That’s math. Your enterprise code signing certificates? The ones protecting your software distributions right now? They’re sitting ducks. Every single RSA-2048 and ECDSA certificate you own will be worthless the moment a sufficiently powerful quantum computer comes online. Most enterprises have zero post-quantum cryptography strategy.

Data Protection Governance Security Compliance Ransomware.