PureCipher Develops Artificial Immune System(TM) Framework to Address AI Data Poisoning and System Compromise

New York, New York--(Newsfile Corp. - May 20, 2025) - PureCipher Inc., a mission-driven deep-tech company specializing in secure-by-design AI infrastructure, announced the development of its proprietary Artificial Immune System™ (AIS) - a defense framework designed to detect, contain, and mitigate two of the most critical vulnerabilities facing artificial intelligence today: data poisoning and model compromise.

As enterprises and governments deploy increasingly autonomous and embedded AI systems, the threat of sabotage is no longer theoretical. From healthcare diagnostics and financial underwriting to battlefield logistics and critical infrastructure, AI is now making high-impact decisions in real time, often without human involvement. In this context, PureCipher's AIS delivers a new class of embedded security: one that ensures the internal trustworthiness of AI systems, not just their perimeter defenses.

"AI is not just another software tool it is a decision-making agent," said Wendy Chin, Founder and CEO of PureCipher. "And like any agent, it can be misled, hijacked, or corrupted from within. Our Artificial Immune System™ framework was designed to protect AI models the same way our bodies protect themselves - with constant surveillance, adaptive response, and built-in resilience."

The Growing Threat Landscape: Poisoned Data and Logic Drift

The development of AIS is PureCipher's response to two rapidly escalating threats:

• AI data poisoning involves the deliberate tampering and insertion of corrupted or malicious data into a model's training set, resulting in warped logic, backdoor behaviors, or targeted bias.
• System compromise refers to attacks that occur post-deployment, where AI models are modified via unauthorized access, tampered APIs, or logic injection, often without detection or traceability.

Unlike traditional malware or denial-of-service attacks, these threats are invisible to perimeter-based cybersecurity tools. Poisoned models often appear functional until they are triggered under specific inputs or real-world conditions. In mission-critical domains, such as autonomous vehicles or real-time threat detection, such failures can be fatal.