Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The rush to work faster with artificial intelligence (AI) risks encouraging employees to accidentally put sensitive data at risk. Take this scenario: someone in the procurement team has a tight deadline, so they upload a confidential contract into an AI tool to review a few redlines. It’s unclear if the AI system is storing the data from the contract, how long it’ll be retained, and if the data will resurface in a future prompt to someone else.

By James Rees, MD, Razorthorn Security.

Earlier this year, security researchers found more than 1 million records, including user data and API keys, in an exposed DeepSeek database. This massive exposure event tells us that data exfiltration risk and AI proliferation are forever linked together: as AI tools grow in popularity and complexity, exfiltration risk rises in kind.

Modern organizations are becoming increasingly reliant on agentic AI, and for good reason: AI agents can dramatically improve efficiency and automate mission-critical functions like customer support, sales, operations, and even security. However, this deep integration into business processes introduces risks that, without proper API security, can compromise sensitive data and decision-making.

The sheer volume of cybersecurity vulnerabilities is overwhelming. In 2024, there were 39,998 CVEs — an average of 109.28 per day! This constant stream of new threats makes it increasingly difficult for security teams to keep up. Large Language Models (LLMs) offer a possible solution, helping automate vulnerability investigation and prioritisation, allowing teams to more efficiently assess and respond to emerging risks. Do you even have time to skim over 109 CVEs a day?

Learn how AI for automation is set to enhance IT processes, boost efficiency, and drive innovation in this comprehensive guide.

In my security research role at Corelight, I often have to go through large, complex data sets to detect subtle anomalies and threats. It reminds me of a famous quote by Abraham Lincoln: Give me six hours to chop down a tree and I will spend the first four sharpening the axe. For me, that means investing time up front to build tools that allow a large language model (LLM) to do the heavy lifting on key tasks, namely those that teams of analysts would have handled in the past.