John sits down with Shreyans Mehta, CTO and co-founder of Cequence Security, to discuss how AI and machine learning can be applied to improve cloud security. They provide valuable insights for security teams looking to leverage AI to protect their cloud environments and applications.
The conversation focuses on using AI for security use cases like detecting anomalies and suspicious behavior, identifying misconfigurations, and automating response. Shreyans shares real-world examples of how Cequence Security has developed AI models to analyze network traffic, APIs, logs, and other data sources to detect threats targeting cloud applications and infrastructure.
Questions we answer in this episode:
- How can AI improve visibility across cloud environments?
- What are some common use cases for AI in cloud security?
- How can AI help overburdened security teams?
Key Takeaways:
- AI excels at detecting subtle anomalies and identifying emerging threats based on learned patterns.
- AI models must be continuously trained on new data to remain effective as attacks evolve.
- AI augments human analysts by automating tedious tasks so they can focus on higher-value security activities.
- An advantage businesses have in the AI arms race with attackers is being able to train their AI on their unique user activity patterns.
This insightful discussion highlights the transformative potential of AI to improve threat detection, investigation, and response. Security teams looking to apply AI can come away with a better understanding of where to start and how to build an effective AI strategy. John and Shreyans explore key considerations around data quality, model accuracy, and responsible AI practices.
Overall, this episode delivers practical guidance to help security leaders successfully navigate the AI landscape. Listen in to learn how to harness the power of AI to advance your cloud security program.
Links & Notes
Got a question about cybersecurity, AI, or something else related? Ask us here, and we’ll get to it in a future episode!
