Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

At Tines, we take pride in both the flexibility and security of our platform: it’s what allows us to do things like safely connect to any HTTP API in the world, and seamlessly deploy in fully air-gapped environments. Similarly, our AI capabilities have been designed from the ground up to be secure and private, with no logging, internet transit, or training on your data.

The increasing adoption of generative artificial intelligence platforms by threat actors, cyber defenders, and the average organization will present enterprises with an unprecedented number of cybersecurity issues in the coming years, according to a new Gartner report and information from Trustwave subject matter experts.

Snyk’s Security Labs team aims to find and help mitigate vulnerabilities in software used by developers around the world, with an overarching goal to improve the state of software security. We do this by targeting tools developers are using, including new and popular software solutions. With the meteoric rise in AI tooling – specifically the fast-growing field of AI-enabled development environments – we have been including such software in our research cycles.

Leveraging AI is incredibly useful when orchestrating and automating your most important workflows. And it’s essential that you have the right AI model for your organization to handle those workflows as expected. Today, we’re excited to announce that you can select your preferred AI model when using AI in Tines products and features.

Next week marks a pivotal moment for Zenity as we gather for our Sales Kickoff (SKO). While SKOs are traditionally about aligning teams on goals and strategies, ours represents much more than that. It’s a celebration of the massive growth in the AI Agent space, the opportunities it creates, and our recommitment to supporting customers as they navigate this transformative and increasingly security-conscious era.

AI systems bring transformative capabilities to industries like healthcare but introduce unique challenges in protecting patient data. Unlike traditional applications, AI systems rely on conversational interfaces and large datasets to train, test, and optimize performance, often including sensitive patient information. AI systems pose complex risks to patient data privacy and AI data security that cannot be effectively managed using traditional methods.

As C-suites and boards are bombarded with headlines about AI revolutionizing cybersecurity, it’s no wonder they’re putting pressure on SOC leaders to adopt AI. The promise of AI in the SOC is rightfully alluring. An AI-native autonomous SOC has the potential to create a world where AI Agents collaborate with each other to take care of repetitive tasks and handle the majority of low-level alerts, freeing your human team up for strategic, proactive work. The hurdle?

From design to deployment, the rise in AI tools and AI-generated code is changing developers’ workflows, enabling them to focus on more creative and complex tasks. However, while 96% of developers use AI coding assistants to streamline their work, it can have a negative impact on security teams. One-fifth of AppSec teams surveyed said they face significant challenges securing AI-generated code due to how quickly it’s produced.