AI Security: Risks, Myths, and Best Practices - Webinar with Ashish Rajan, CISO of Kaizanteq
Security teams today face a dual challenge: protecting AI systems from external threats while securing the APIs that power them. The reality is clear—if your APIs aren’t secure, neither is your AI.
A BreachForum user came out claiming to have breached OmniGPT and shared samples of stolen data to back up this claim. Weeks later, researchers are still scrambling to figure out the scope, attack method, and more.
Today’s cyber landscape is littered with threats, risks, and vulnerabilities. Every week, we are seeing an increase not only in attacks, but also in the methods used to attack. This week, a new family of malware was discovered exploiting Microsoft’s Graph API.
AI security and API security run alongside each other, much like a double rainbow. Each one contains a full spectrum of security requirements that work in tandem with one another.
AI is revolutionizing industries at an unprecedented pace. But as organizations integrate AI into their workflows, they are encountering serious security risks. In fact, 97% of organizations using generative AI have reported security incidents. Traditional security tools are failing to keep up, leaving companies vulnerable to data breaches, adversarial attacks, and compliance risks.
We’re only a month into the new year, and already, the internet is buzzing with news about AI. Most recently, China’s AI platform DeepSeek has been making whale-sized waves in the cyberworld. But a week after the launch of its new model on January 20th, a tsunami-sized wave hit the system.
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