Can AI Learn to Hack? Machine Learning and Cybersecurity

AI can help both white-hat and black-hat hackers... but which side will win? The AI cybersecurity debate continues.

Welcome (back) to AI Minds, a newsletter about the brainy and sometimes zany world of AI, brought to you by the Deepgram editorial team.

In this edition:

  • 🛡️ How to use AI to detect cybersecurity threats

  • 🧠 Cybersecurity experts’ framework for understanding adversarial AI

  • 🏢 Generative AI hype is a cybersecurity risk for businesses

  • ⚔️ Adversarial Machine Learning: Everything you need to know

  • ⛰️ How adversarial examples build resilient machine learning models

  • 🔊 Webinar: Building Voice AI Agents

  • 💾 Ranking Different LLMs’ Generated Code from Worst to Best

  • 🐦 Microsoft and Google on AI’s Impact on Cybersecurity

  • 📲 3 New Trending AI Apps to Check Out!

  • 🎙️ AI Minds Podcast, featuring Ulysse Maes

  • 🎉 IBM on whether AI will help or hurt cybersecurity?

  • 🎒 Harvard: Prepare for AI Hackers

  • 🤖 Bonus Content! The only guide you need on AI Agents

  • 📶 More Bonus Content! How to Jailbreak an LLM

Thanks for letting us crash your inbox; let’s party. 🎉

Deepgram just released a brand new text-to-speech model called Aura! Check it out here. 🥳

🧑‍🔬 The Cybersecurity Risks of AI… and Beyond

The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities -  In this paper, traditional approaches to vulnerability analysis are juxtaposed with AI-driven methodologies, highlighting the efficacy of automated scanning, threat prioritization, and adaptive risk assessment. It also delves into the role AI-driven automation plays in expediting incident response, minimizing human error, and fortifying overall security postures.

Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI -  This paper explores the multifaceted dimensions of AI-driven cyberattacks, offering insights into their implications, mitigation strategies, underlying motivations, and profound societal impacts.

AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business - The rush to buy into the hype of generative AI and not fall behind the competition potentially exposes companies to cyber-attacks or breaches. This paper outlines significant cybersecurity threats generative AI models pose, including potential ‘backdoors’ in AI models that could compromise user data.

🏇 Adversarial Concepts in AI: From Training to Hacking

Glossary Entry: Adversarial Machine Learning - At its core, Adversarial Machine Learning (AML) involves crafting inputs specifically designed to deceive machine learning models—inputs that appear benign to human observers but wreak havoc on the algorithms that underpin AI systems. This dualistic nature of AML makes it a double-edged sword...

How adversarial examples build resilient machine learning models - Since deep neural network (DNN) adversarial examples were highlighted and popularized in 2013, a maturing subfield of AI research has investigated how to make AI, especially DNNs, less vulnerable to these adversarial attacks. Much of this research focuses on discovering and defending against new exploits. Here’s how far they’ve come.

🤖 Upcoming Webinar! Build AI Agents With Voice

AI agents that can understand and respond to users via natural voice communication are the future of customer interactions and personal assistance. In our upcoming webinar, Michelle Chan, Product Manager at Deepgram, will share her expertise on how to build efficient conversational AI workflows with voice.

Sign up here!

When: April 10, 2024 - 9:00am PT | 11:00am CT | 12:00pm ET

Where: Online

📽️ Ranking Different LLMs’ Generated Code from Best to Worst

Of course, before AI can learn to hack, it must first learn to code. That’s why we put a few well-known AI coders (read: LLMs) to the test. In this video, four major AI players—StarCoder, Gorilla, ChatGPT, and Github Copilot—are given two “technical interview” questions. The first is a classic Leetcode-style problem, while the second tests their ability to use an API.

🐝 Social media buzz

The GPT-4 Security Checklist Generator is an AI-powered tool that automatically generates security checklists for developers. It aims to make threat modeling and security analysis easy and accessible for all developers.

CyberRiskAI offers a comprehensive cybersecurity risk assessment service to help organizations identify and mitigate cybersecurity risks. Their risk assessments provide valuable insights into potential vulnerabilities, enabling businesses to prioritize security efforts and build trust with partners. is an end-to-end financial fraud and risk management platform designed to help companies prevent fraud, increase revenue, and create frictionless customer experiences. The company offers a customizable and comprehensive suite of AI-powered fraud detection and prevention products to ensure trust at every step of the digital customer journey.

🎙️ AI Minds Podcast! 

In this episode of the AIMinds, we get in to a conversation with Founder and CEO of Scribewave, Ulysse Maes. 

Ulysse, an entrepreneur coming from a background in business engineering and music, shares the journey behind the creation of his transcription platform. 

Starting with the desire to make lyric videos for his songs, his venture took an unexpected turn toward speech-to-text translations.

He discusses the evolution of Scribewave, its growth leveraging AI indexing, and his recent focus on marketing efforts to raise global awareness.

🤖 Additional Bits and Bytes

And, of course, since you’ve scrolled down this far, you get some (relatively random) juice bits of content!

  • Will AI Help of Hurt Cybersecurity? Definitely! The average cost of a data breach is $4.5 million dollars. However, AI is estimated to represent a potential savings of $1.76 million per data breach. In this video, IBM’s Jeff Crume shows how artificial intelligence can be exploited by hackers AND how it can be used by cybersecurity experts to better defend your company against attacks. 

  • Harvard: Prepare for AI Hackers - As the title suggests, hackers are now starting to use AI to expedite their “businesses.” Both black hats and white hats alike. Learn more here! 

  • Glossary: AI Agents - On a lighter note, this glossary entry delves deeper into the domain of AI agents. You got a sneak peek into this world with the AI Coders video above, but if you’d like to see just how deep the rabbit hole goes, click this link.

  • How to Jailbreak an LLM - We’ve featured this article in the edition on Prompt Engineering. So if you’d like to learn more about how to get past a public LLM product’s safety protocols using nothing but natural language, click here.