Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your AI Agent just updated a vendor’s payment details in your Enterprise Resource Planning (ERP) system based on a seemingly harmless prompt. No data was exfiltrated. No access policy was violated. But now, a $250,000 payment is sitting in a fraudulent bank account. This is the new face of AI risk. As enterprises adopt AI Agents - either off the shelf or custom built, security teams are facing a fast-moving shift.

Threat Intelligence (TI) has become the secret weapon of modern security teams—essential for identifying possible emerging threats before they escalate. But TI is only as valuable as its accuracy, relevancy, and timeliness. Unfortunately, many traditional TI approaches can no longer keep up, as security teams are plagued with information overload: too many signals, too little context, and limited resources to process everything. This is why the coupling of GenAI and TI is a game changer.

‍ ‍The European Central Bank (ECB), tasked with maintaining financial stability in the region, is changing how it supervises institutional resilience. According to a July 2025 Reuters report, banks across the Eurozone are being asked explicitly to model how large-scale disruptions, including geopolitical conflict, potential dollar shortages, and cyber incidents, could impact their capital reserves.

Trapped in silos. Drowning in noise. Valuable insights are lost in a flood of disconnected systems. Critical information rarely reaches leadership in a form that begs action.

Your external attack surface is growing — whether you’re aware of it or not. Cloud migration, IoT, AI, and remote work are all contributing to the rapid expansion of organizations’ external attack surfaces, and many security teams are struggling to keep up. According to a 2021 report, 69% of organizations admitted they had experienced at least one cyberattack that was initiated through exploiting an unknown or unmanaged internet-facing asset.

The increasing deployment of large language models (LLMs) in enterprise environments has created a pressing need for effective security testing methods. Traditional approaches, relying heavily on predefined templates, are limited in comparison to adaptive attacks — particularly those related to prompt injection attacks. This limitation becomes especially critical in high-performance computing environments where LLMs process thousands of requests per second.

Many of the largest hospitality organizations operate on a global scale. While guest demands remain relatively constant across regions, cyberthreats and defensive capabilities can vary significantly. Trustwave SpiderLabs recently released the 2025 Trustwave Risk Radar Report: Hospitality Sector, providing updated insights and strategies to enhance data security across the industry.

As AI-generated code continues to boost developer productivity – and with it the number of vulnerabilities in code – the need for a programmatic approach to security within a fully AI-enabled reality is key. AI Trust and governance is the new standard for the AI era, and is achieved through visibility, prioritization, and policy. With this in mind, over time, Snyk has expanded the number of reports and analytics provided in its platform to address this need.

Organizations spend enormous effort fixing software vulnerabilities that make their way into their public-facing applications. The Consortium for Information and Software Quality estimated that the cost of poor software quality in the United States reached $2.41 trillion in 2022, a number sure to be much higher today. That’s nearly 10% of the current GDP within the US. As we will show, it makes sense that the cost of poor software quality is so high.