GenAI has the promise to transform companies, and introduce a lot of security risk. One of the main benefits of GenAI relates to the modernization of apps. Most companies are going through some type of app modernization. They are responding to the market by delivering better and better experiences to their customers. This is largely done through the experience people have with their apps. This ranges from banking to healthcare to travel and everywhere in between.

The rise of Generative AI models has sparked a transformative shift across industries, offering unprecedented capabilities in automating tasks, enhancing decision-making, and fostering innovation. As publicly available tools, these models promise a democratization of technology, suggesting that any company can leverage them to boost efficiency and creativity. However, the reality of integrating and optimizing these AI models for specific corporate needs is far from plug-and-play.

Snyk has been a pioneer in AI-powered cybersecurity since the launch of Snyk Code in 2021, with the DeepCode AI engine bringing unmatched accuracy and speed to identifying security issues in the SAST space for the first time. Over the last 3 years, we have seen the rise of AI and LLMs, which Snyk has been at the forefront of with the introduction of new AI-based capabilities, such as DeepCode AI Fix, our vulnerability autofixing feature, or our third-party dependency reachability feature.

With the widespread use of AI technology, numerous AI models gather and process vast amounts of data, much of which comprises personal information utilized to offer personalized experiences. However, this abundance of data poses inherent risks, particularly in terms of privacy and security.

The Sysdig Threat Research Team (TRT) recently observed a new attack that leveraged stolen cloud credentials in order to target ten cloud-hosted large language model (LLM) services, known as LLMjacking. The credentials were obtained from a popular target, a system running a vulnerable version of Laravel (CVE-2021-3129). Attacks against LLM-based Artificial Intelligence (AI) systems have been discussed often, but mostly around prompt abuse and altering training data.

Amazon Bedrock and Elastic’s Attack Discovery automate security analyst workflows As cyber threats grow increasingly sophisticated, the need for highly effective security measures becomes imperative. Traditional SIEMs aren’t equipped to address threats fast enough because they rely on too many manual and labor-intensive tasks. AI-driven security analytics from Elastic’s Search AI platform solves these challenges.

Prioritize attacks, not alerts, with new Attack Discovery capability, powered by Search AI.

Language models have revolutionized natural language processing, enabling machines to generate human-like text with remarkable fluency and coherence. However, despite their impressive capabilities, traditional language models often need help with knowledge-intensive tasks that require factual accuracy, external knowledge integration, and contextual awareness.