Following the Sysdig Threat Research Team’s (TRT) discovery of LLMjacking — the illicit use of an LLM through compromised credentials — the number of attackers and their methods have proliferated.

Generative AI (GenAI) has the power to transform organizations from the inside out. Yet many organizations are struggling to prove the value of their GenAI investments after the initial push to deploy models. “At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value,” according to Gartner, Inc.

Snyk Learn, our developer security education platform, just got better! We have expanded our lesson coverage and created a new learning path that covers the OWASP Top 10 for LLMs and GenAI, and is entirely free! As AI continues to revolutionize industries, ensuring the security of AI-driven systems has never been more critical.

Artificial Intelligence (AI) has evolved into a vital part of modern businesses. Its reliance on large amounts of data drives efficiency and innovation. However, the need for data security in AI systems has grown critical with this increasing dependence on AI. Sensitive data used in AI must be protected to avoid breaches and misuse. This post will explore critical threats to AI data security, discuss mitigation techniques, and present best practices to help organizations safeguard their AI systems.

As more companies leverage Artificial Intelligence (AI) and Large Language Models (LLMs) to maximize productivity and accelerate growth, the responsibility of safeguarding data has become increasingly critical. In this environment, robust access controls are not just a security measure but a fundamental aspect of responsible AI usage. This article will explore what access controls are, why they are essential for AI and LLM security, and how organizations can implement them effectively.

From the moment ChatGPT was released to the public, offensive actors started looking to use this new wealth of knowledge to further nefarious activities. Many of the controls we have become familiar with didn’t exist in its early stages. The ability to request malicious code or the process to execute an advanced attack was there for the asking from an open prompt. This proved that the models could provide adversarial recommendations and new attacks never before seen.

Phishing attacks occur when cybercriminals trick their victims into sharing personal information, such as passwords or credit card numbers, by pretending to be someone they’re not. Artificial Intelligence (AI) has made it easier for cybercriminals to carry out phishing attacks by writing believable phishing messages, mimicking people’s voices, researching targets and creating deepfakes.

As organizations increasingly integrate artificial intelligence (AI) into their operations, the nature of data security is undergoing significant transformation. With AI’s ability to process vast amounts of data quickly, the risk of data breaches and leaks has grown exponentially. In this context, Data Loss Prevention (DLP) has (re)emerged as a critical component for IT professionals seeking to safeguard sensitive information.

As businesses increasingly adopt Generative AI (Gen AI) to enhance operations, customer engagement, and innovation, the need for robust AI guardrails has never been more critical. While Gen AI offers transformative potential, it also introduces significant risks that can jeopardize your business if not properly managed. Below, we explore five critical risks associated with Gen AI and provide strategies to avoid them.

AI guardrails are vital for ensuring the safe and responsible use of AI/large language models (LLMs). However, focusing solely on single prompt-level checks can leave organizations vulnerable to sophisticated threats. Many company policy violations and security risks can be cleverly split across multiple, seemingly innocent queries. To effectively protect against these threats, a more comprehensive approach is needed — session-level monitoring.