Securing the AI Era: Addressing Emerging Risks in AI Applications
episode
The Fast Mode Podcasts: Breaking News, Analysis and Updates From Telecoms Industry
25 min
1 speaker
4 chapters
transcribed 1 month ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the main topic discussed in this episode?
Welcome to the Fast Mode Podcast series. I'm Tara Neil, and with me today is Tomar Avni, VP of Product at Tenable AI Security. Tenable, the exposure management company, exposes and closes the cybersecurity gaps that erode business value, reputation, and trust. The company's AI-powered exposure management platform radically unifies security, visibility, insight, and action. across the attack surface, equipping modern organizations to protect against attacks, from IT infrastructure to cloud environments to critical infrastructure and everything in between. Welcome Toma. Great to have you on today's episode.
Thank you so much for having me.
All right, awesome. Okay, so let's dive right in. Um let's talk about, you know, um the vulnerabilities and security gaps um that we find um, you know, in AI applications. And how do these um gaps compare um to traditional applications?
Yeah, for sure. So, you know, I think uh the vulnerabilities and the security gaps uh that AI applications and platforms are introducing are inherently different than the kind of um vulnerabilities and security gaps that we've been used to uh in the last few decades in the security domain. Um and I I would I would uh explain why. So I think there are three main categories that you can think about when you think about AI vulnerabilities. And AI risks. Those are around data exposure, manipulation of the AI outputs and malicious actions that can be taken. So So data exposure, think about things like your co-pilot studio exalting data, your most sensitive data to the outside world. Manipulation of outputs might be an internal AI application that was built.
Manipulation. For all of your employees. And if any manipulation is happening, then employees are taking decisions and actions based on outputs that are actually manipulated and not true. And then malicious actions, I think, are the most severe one because as any AI system is becoming agentic, and that means basically autonomous or being able to take actions, then malicious. That's actions they can can take place and vulnerabilities can actually make the AI take the wrong actions. And so these are the three uh the three different risks that exist uh with uh with AI. And I think as mentioned, they're very different than what uh we've been used to, and especially the malicious actions. I mean, in the past, uh the software that we've been using or the technology that we've been using within companies couldn't really take its own actions without human oversight.
But now it is possible. And so when malicious actions take place, you know, a whole slew of of of bad things can happen to uh to companies and and organizations. I think the last point that maybe I want to mention on this is uh around shadow AI. So, you know, the there there always been shadow IT, right? Yeah, yeah. And it sounds like maybe would would be sound for some people that it's kind of the same thing. But it's actually very different because AI is embedded in each and every thing that we do. So it's not just in the SaaS, right, the software as a service that That we're using as people, as employees. It could also be an agent that is developed on the endpoint of a user. Or it could be maybe a cloud resource that actually has some AI components.
And so AI is everywhere, and so shadow AI is everywhere. And that's why the shadow AI risk is very different and on a very different level than the shadow IT that we were used to in the last couple of.
Mm. You're right. Because I I think we are starting to hear this thing about AI governance, you know, in software development and also in the use of cloud and SARS app. So you you you know, you you pointed out the right thing, especially on shadow AI. In fact, uh even I was thinking that shadow A AI would just be um, you know, your employees use uh using uh applications like Chat GPT, things like which are not really authorized. But um You know, to basically get some work done. But now that you pointed out some other ways where m shadow AI actually creeps in because, you know, like uh you know, endpoint and, you know, some cloud resource may actually employ some some form of uh, you know, a uh gen AI and AI, right?
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Chapters
4 chapters
1
What is the main topic discussed in this episode?
0:11–5:00
2
What unique security gaps do AI applications have compared to traditional software?
5:00–8:30
3
How do data exposure, output manipulation, and malicious actions create AI‑specific risks?
8:30–15:25
4
What is “shadow AI” and why does it pose a greater threat than traditional shadow IT?
15:25–24:51
Speakers
1 identifiedMore from The Fast Mode Podcasts: Breaking News, Analysis and Updates From Telecoms Industry
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