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The Latest Cybersecurity News & Insights From Around The World
Understanding Bias in Generative AI: Types, Causes & Consequences
Bias in generative AI refers to the systematic errors or distortions in the information produced by generative AI models, which can lead to unfair or discriminatory outcomes. These models, trained on vast datasets from the internet, often inherit and amplify the biases present in the data, mirroring societal prejudices and inequities.
Seven ways AI could impact the future of pen testing
In an era where attack surfaces are expanding faster than ever, AI has the potential to transform how organizations find and fix vulnerabilities. Gartner estimates AI agents will reduce the time it takes to exploit account vulnerabilities by 50%. From automating routine scans to developing self-learning attack agents, AI is already changing the red team playbook – and the pace of innovation shows no signs of slowing.
The Data Problem: Why LLM Security Is So Complex
The Data Problem: Why LLM Security Is So Complex Large language models are trained on terabytes of data, but what happens when that data is flawed? In this video, A10 Networks' security experts, Jamison Utter, Madhav Aggarwal, and Diptanshu Purwar, discuss a critical, often-overlooked aspect of AI security: the training data itself. They explain that LLMs are inseparable from the data they're trained on, which means if the data contains biases, toxic content, or other vulnerabilities, those flaws are vulnerable to exploitation by attackers.
Interview: Building human & AI synergy with Peter Ruta, Founder / CEO of Arcanna.ai [242]
On this episode of the Cybersecurity Defenders Podcast we speak with Peter Ruta, Founder / CEO, Arcanna.ai.
How to Use the AI Security Assistant for Deeper Security Expertise and Context
In this demo, we will look at three different use cases for the AI Security Assistant including general security knowledge, ticket context and data visualization.
Securing Shadow AI: 6 Principles from Security Leaders Who've Been There
Everyone's racing to use AI right now. But securing AI adoption while maintaining productivity—getting visibility into shadow AI, educating employees without blocking innovation, and building governance that actually works—is harder than it looks. We recently hosted a discussion between Anant Mahajan, Head of Product at Nightfall, and Yunique Demann, VP of Information Security at TPx, to dig into the practical realities of AI governance.
Safeguard: Using the double-edged sword of AI for good
Examine the function of AI in security tools, and how this double-edged sword can be used for good or ill in the cybersecurity sphere. Plus, see its integration into an identity fabric and get a glimpse into its responsible use – paired with the human element – in One Identity Safeguard.