Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How to Ensure Data Privacy with AI: A Step-by-Step Guide

AI sits in everyday workflows: assistants answering customer questions, copilots helping developers, and RAG apps searching internal knowledge. That means personal and sensitive data flows through prompts, vector stores, and integrations you didn’t have a year ago. Privacy can’t be an end-of-quarter compliance push anymore. It needs to live in your pipelines and apps the way logging and monitoring do.

Securing AI Applications in the Cloud: Shadow AI, RAG & Real Risks | Mend.io

What does it take to secure AI-based applications in the cloud? In this episode, host Ashish Rajan sits down with Bar-el Tayouri, Head of Mend AI at Mend.io, to dive deep into the evolving world of AI security. From uncovering the hidden dangers of shadow AI to understanding the layers of an AI Bill of Materials (AIBOM), Bar-el breaks down the complexities of securing AI-driven systems. Learn about the risks of malicious models, the importance of red teaming, and how to balance innovation with security in a dynamic AI landscape. What is an AIBOM and why it matters The stages of AI adoption.

Automation Anywhere + Protecto: How Leaders Secure GenAI Data

GenAI data security is now a critical concern for every enterprise. In this insightful episode of AI On The Edge, Dinesh Chandrasekhar, Founder and Chief Analyst at Stratola, sits down with Amar Kanagaraj (CEO, Protecto.ai) and Steve Shah (SVP Products, Automation Anywhere) to explore the future of data privacy, agentic automation, and securing LLMs in enterprise settings. Learn how two of the industry's top innovators are setting AI guardrails, preventing sensitive data leaks, and embedding privacy-by-design into large-scale automation.

How AI is Transforming Application Security Testing

AI is revolutionizing software development, enabling teams to build and ship faster than ever. But this speed introduces new risks at an unprecedented scale. Your current application security testing program must evolve to keep pace. For security leaders, the challenge is clear: how do you secure applications without slowing down innovation? This article provides a practical analysis of how artificial intelligence is fundamentally transforming application security testing (AppSec).

Cybersecurity, Cyber Recovery and the Fight Against AI

Cybersecurity has always been a high-stakes game of cat and mouse. Defenders build taller walls, and attackers find longer ladders. But with the rapid rise of artificial intelligence (AI), the very nature of this conflict is changing. AI is no longer just a tool for defenders; it’s being weaponized by cybercriminals to automate and scale attacks with unprecedented speed and sophistication.

We Need to Teach Our AIs to Securely Code

I have been writing about the need to better train our programmers in secure coding practices for decades, most recently here and here. At least a third of data compromises involved exploited software and firmware vulnerabilities and we are on our way to having over 47,000 separate, publicly known vulnerabilities this year. There are at least 130 new vulnerabilities learned and publicly reported every day, day after day. That is a lot of exploitation. That is a lot of patching.

Hybrid Detection Architecture: Rules, ML, and LLMs in Concert

Security teams are drowning in complexity. Modern networks generate millions of events daily, attackers constantly shift tactics, and the tools meant to protect us often work in isolation, blind to what their neighbors are seeing. That mythical single solution that would catch everything? It's sitting in the graveyard next to perpetual motion machines and honest vendor pricing.