Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Have you heard about AI yet? Just kidding. We know you have. Recently, AI’s popularity has skyrocketed among businesses and consumers alike. This surge was driven by a combination of technological advancements (e.g., machine learning, natural language processing, and data analytics) with an increase in tool accessibility and user-friendliness.

AI has undoubtedly revolutionized various industries, enhancing both efficiency and innovation through low-code and no-code platforms. Yet, this ease of development brings with it an increased burden of security. As business users and developers rapidly build applications, automations, and bots using AI, the complexity and volume of these creations amplify potential security vulnerabilities.

The use of artificial intelligence (AI) for defense allows for the better scanning of networks for vulnerabilities, automation, and attack detection based on existing datasets. However this is all in defense against an unknown attacker, who can have varying offensive tools all designed to overcome the most sophisticated defense. Is the biggest challenge for defensive AI that there is an offensive AI operator with unknown capabilities? And has offensive AI lowered the barrier of entry for bot attackers?

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing model development.

Cybersecurity has become a battlefield where defenders and attackers engage in a constant struggle, mirroring the dynamics of traditional warfare. In this modern cyber conflict, the emergence of artificial intelligence (AI) has revolutionized the capabilities of traditionally asymmetric cyber attackers and threats, enabling them to pose challenges akin to those posed by near-peer adversaries.

Generative AI tools like ChatGPT and Google Bard are some of the most exciting technologies in the world. They have already begun to revolutionize productivity, supercharge creativity, and make the world a better place. But as with any new technology, generative AI has brought about new risks—or, rather, made old risks worse.

Retrieval-Augmented Generation (RAG) is a cutting-edge strategy that combines the strengths of retrieval-based and generation-based models. In RAG, the model retrieves relevant documents or information from a vast knowledge base to enhance its response generation capabilities. This hybrid method leverages the power of large language models, like BERT or GPT, to generate coherent and contextually appropriate responses while grounding these responses in concrete, retrieved data.