Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Explore enhanced secrets security across code, collaboration tools, & public repos. Discover new Agentic AI protection, NHI lifecycle automation, and smarter incident management.

Global Artificial Intelligence (AI) infrastructure spending is projected to surpass $200 billion by 2028, according to research from the International Data Corporation (IDC). As organizations rapidly deploy more complex AI systems, the demand for high-performance infrastructure, like Graphics Processing Units (GPUs) and AI accelerators, is surging. This growth exponentially increases computing power, energy consumption and data exchange across hybrid and cloud environments.

AI guardrails are structured safeguards, whether technical, security or ethical, which are designed to guide AI systems so they operate safely, responsibly, and within intended boundaries. Much like highway guardrails that prevent vehicles from veering off course, these measures ensure AI remains aligned with organizational policies, regulations, and ethical values.

Shadow AI refers to the unauthorized or unmonitored use of AI tools (like ChatGPT, Copilot, Claude, and Gemini) by employees in the workplace. It’s now one of the fastest-growing data exfiltration vectors. Employees are pasting source code, customer or patient data, contract terms, and even M&A info into gen AI tools, often without realizing the risk. And many legacy DLP tools are still catching up.

For a AI software company like Riscosity, which helps organizations secure and govern data flows to third parties, compliance is not just a regulatory requirement—it is central to the value proposition. Recognizing this, Riscosity has launched a dedicated Trust Center at trust.riscosity.com, powered by industry leader Vanta, to streamline how it communicates its compliance posture with current and prospective customers.

Data poisoning is a type of cyberattack where malicious actors deliberately manipulate or corrupt datasets meant for training machine learning models, especially large language models (LLMs). Tampering parts of a raw data set with an incorrect, often duplicitous one can negatively impact the result in various ways. Fundamentally, it aims to alter how AI models learn information so that the output is flawed.