Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI has made good on its promise to deliver value across industries: 77% of senior business leaders surveyed in late 2024 reported gaining a competitive advantage from AI technologies. While AI tools allow developers to build and ship software more efficiently than ever, they also entail risk, as AI-generated code can contain vulnerabilities just like developer-written code. To enable speed and security, DevSecOps teams can adopt tools to integrate security tasks into developer workflows.

How good is Symbolic AI as an automated expert system for analyzing code paths and detecting security vulnerabilities? Snyk Code is tested and demonstrates the benefits of rule-based, algorithmic systems.

Businesses must manage IT operations and cybersecurity in the day-to-day and ever-evolving digital world, which is becoming more and more complicated. Cyber threats, system warnings, and the increasing amount of data are too much for traditional IT management techniques to handle. This is where artificial intelligence for IT operations changes the game.

Enterprise Privacy Management with Feroot AlphaPrivacy AI provides automated privacy compliance monitoring and enforcement across global enterprise environments, ensuring compliance with multiple regulations and standards.

Now that six in ten security leaders view AI as a “game changer” across all security functions and 85% of security professionals report increased AI investment and usage in the past year, it’s clear that AI is no longer a fringe technology in security operations. But the AI conversation has evolved recently as a new buzzword has taken over: agentic AI.

With the announcement of Anthropic’s Claude 3.7 Sonnet model, we, as developers and cybersecurity practitioners, find ourselves wondering – is the new model any better at generating secure code? We commission the model to generate a classic CRUD application with the following prompt: The model generates several files of code in one artifact, which the user can manually copy and organize according to the file tree suggested by Claude alongside the main artifact.

Please see part Part I for our introduction to leveraging AI in the enterprise and Part II for why the legacy landscape falls short in the age of GenAI.