Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Many tech professionals see integrating large language models (LLMs) as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs securely and effectively. This blog shares our journey of integrating cutting-edge AI into a security product.

The GDPR sets out clear principles for how personal data should be handled. Here are some that AI tools can easily clash with.

Over the past few years, enterprises have rapidly integrated GenAI into an increasing number of workflows and use cases. Amidst the rush and excitement to adopt a free tool that significantly boosts productivity, business leaders de-prioritized privacy, till it became a compliance issue. As privacy tools offering a quick patch quickly flooded the market, businesses ran into a new problem – which is the best tool?

The rise of generative AI has fundamentally changed how we work, create, and collaborate. But as organizations rush to integrate AI tools into their workflows, they're inadvertently creating entirely new categories of data risk that traditional security measures weren't designed to handle.

The speakers at ShowMeCon 2025 explored why policy isn't protection without validation. AI, identity, and threat detection must align to reduce operational risk.

The 2025 Gartner Security & Risk Management Summit was a wake-up call, and an opportunity, for anyone responsible for securing the future of AI. With over 1,700 AI use cases now reported across federal agencies and enterprise adoption growing at a breakneck pace, the message was clear: AI is no longer on the horizon. It’s here, it’s active, and it needs securing.

Businesses are moving beyond AI experiments and proofs of concept. As we approach what IDC is predicting will be the “AI pivot years” of 2025-2026, organizations are prioritizing, planning, and building for scale. This shift includes AI agents — self-directed tools that automate tasks — as technology providers strive to simplify development workflows. Under the surface, AI systems expose an expanded threat landscape that spans the software development lifecycle (SDLC).