Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In order to collect various security-related metrics, Bitsight scans the entire internet, collecting a unique set of data that enables us to carry out a variety of studies that would be extremely difficult for any other company to conduct. One of the metrics that we collect is related to the presence of certain vulnerabilities. For this, we need to take into consideration all possible mitigation strategies that are available and that allow us to reduce the risk.

Recent cyberattacks on retailers in the UK and the U.S. are now shifting to target the financial sector, with news on breaches reaching headlines almost daily. These stories track how today’s threat actors operate: they are strategic, pivot quickly, exploit weak links and are highly opportunistic. This opportunism means that if threat actors discover an unlocked door in one business within an industry, they will try every door within that industry to find a common weakness.

Security teams are drowning in findings, but only a fraction of exposures actually put the business at risk. Treating every issue as equal spreads resources thin, slows down remediation, and leaves critical systems exposed. Threat Exposure Management (TEM) changes the equation by forcing teams to focus on the exposures most likely to cause real damage – and to build the operating model that ensures they get fixed.

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.