Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Connected devices are powering transformation in every sector, whether it’s smart meters in energy, robotic arms in manufacturing, or infusion pumps in healthcare. But alongside innovation comes risk. More than 50% of connected devices have a known vulnerability, and with security breaches in IoT rising year over year, it’s no longer enough to bolt on protection after the fact.

On July 23rd, the White House released America’s AI Action Plan, a sweeping federal strategy to drive U.S. leadership in artificial intelligence. The message was loud and clear: AI is a national imperative. The plan calls for removing regulatory barriers, investing in infrastructure, and accelerating AI adoption across commercial and government sectors. For data security leaders, this signals a pivotal shift.

The adage ‘an ounce of prevention is better than a pound of cure’ applies to AppSec vulnerability management. Traditionally, AppSec has focused on a reactive ‘curing flaws’ paradigm, identifying and fixing vulnerabilities after they have occurred. However, the never-ending escalation between threats and security leads to alert fatigue and security debt.

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.

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.

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.