Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Future (and Present) of the Internet, AI, and Tech with Nicholas Thompson

This week, host João Tomé is joined by Nicholas Thompson, CEO of The Atlantic and former editor-in-chief of Wired, during his participation at Web Summit, the international event held in Lisbon with over 70,000 attendees. In this conversation, Thompson discusses the Internet, AI, social media, and the challenge of protecting content creators from AI crawlers—a problem Cloudflare’s AI Audit is designed to address. We also explore the 2024 media landscape, its future, and its role in supporting democracy.

EP 66 - Post-Election Insights: AI, Misinformation and Security

In this episode of Trust Issues, host David Puner interviews James Imanian, Senior Director of the U.S. Federal Technology Office at CyberArk. They discuss the critical topic of election security, focusing on the recent 2024 U.S. presidential election. Drawing from his extensive background in cybersecurity including a career in the Navy and a stint at the U.S.

Planning with AI: Minimizing Uncertainty, Maximizing Trust

Gal Peretz is Head of AI & Data at Torq. Gal accelerates Torq’s AI and data initiatives, applying his deep learning and natural language processing expertise to advance AI-powered security automation. He also co-hosts the LangTalks podcast, which discusses the latest AI and LLM technologies. To stay ahead of today’s threats, you must do more than keep pace — you need to equip your team with tools that enable smarter, faster responses.

Falcon Data Protection AI-Powered Anomaly Detections: Demo Drill Down

Sensitive data handling often risks accidental exposure. CrowdStrike Falcon Data Protection, part of the powerful CrowdStrike Falcon platform, uses AI-driven anomaly detection to prevent unauthorized data transfers. In this demo, see how quickly Falcon identifies and blocks an attempted transfer of customer PII to a personal Google Drive, generating real-time alerts to help security teams act fast.

AI Tokenization: Understanding Its Importance and Applications

In artificial intelligence (AI), especially within natural language processing (NLP), tokenization is a fundamental process that breaks down text into smaller, manageable units known as tokens. Depending on the specific task and model, these tokens can be individual words, subwords, characters, or even symbols.

7 Tips from a Security CTO for Balancing AI Innovation with Governance

As a modern CTO, it should probably come as no big surprise that I’m an optimist on the innovative prospects of artificial intelligence (AI). But I’ve been in this career for a long time, and that optimism is tempered with experience. I’ve seen enough emerging technology patterns to know that it always takes a lot more time and resources than people think to evolve innovative technologies beyond their final barriers.

Redefining Modern Security with the Introduction of the Arctic Wolf Aurora Platform, Powered by Alpha AI

In today’s rapidly evolving threat landscape, where cyberattacks grow more sophisticated by the day, staying ahead requires more than vigilance—it demands a platform built to operate at scale. Enter the Arctic Wolf Aurora Platform, the new name for our industry-leading security operations platform. With the ability to process over seven trillion events weekly, The Aurora Platform stands as one of the largest and most advanced cybersecurity platforms in our industry.

The Rise of AI Agents in the Enterprise

AI Agents have become indispensable in modern enterprises, driving efficiency, innovation, and competitive advantage. These agents, which can perform tasks ranging from simple automation to complex decision-making, are transforming how businesses operate. The adoption of AI agents is widespread, with companies leveraging them to enhance customer service, streamline operations, and gain insights from vast amounts of data.

Step 3. How to automatically validate AI-generated Fuzz Tests

After generating fuzz tests with LLMs, the next important step is verifying that these tests are of high quality and ensuring they run and work as intended. CI Fuzz can now automatically build the generated fuzz test, run it, and perform a health check to assess its quality and refine it further if it doesn't pass the health check. Watch the video to see it in action.