Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

“72% of organizations use AI in business functions — but only 13% feel ready to secure it.” That gap, between adoption and preparedness, explains why traditional AppSec approaches aren’t enough.

AI attacks pose real risks for companies because of their ability to scale and automate attacks like brute force attacks, smarter malware, deep fakes and advanced phishing. Attacks that were once slow, manual and easy to spot are now becoming faster, more sophisticated and harder to detect. UK government research shows that 32% of UK businesses have experienced a cyber attack in the last year, and experts warn that AI could make this number rise significantly.

The SolarWinds supply chain attack in 2020 reminded the world how a single weakness in trusted software can have global consequences. That incident reshaped how organizations view software integrity and the importance of securing every stage of the development pipeline.

Large language models take in unstructured data. They transform it into context, embeddings, and answers. That journey touches raw files, vector stores, model logs, and third-party services. Traditional privacy programs focus on databases and forms. LLMs push risk to the edges. The riskiest moments are when you ingest messy content, when your system retrieves chunks to support an answer, and when an agent with tool access is tricked into over-sharing.

Assigning remediation tasks across an enterprise organization can feel like navigating a maze of inconsistent tags, overlapping teams, and unclear ownership. It’s one of the most persistent operational challenges in vulnerability and exposure management, and one of the biggest barriers to speed. Each scanner and cloud platform comes with its own tagging logic. One system uses ProductOwner, another productowner. Some tags are outdated, others duplicated, and many have no clear purpose.