AI Security DNA Architecture: A Bio-Inspired Model for Inherited Software Safety
Sergei Achimov · 2025
As artificial intelligence systems become increasingly autonomous, self-replicating, and capable of recursive code generation, traditional safety mechanisms—implemented purely in software—are proving insufficient. In this paper, I propose a bio-inspired architecture for AI safety based on two foundational principles: immutable DNA-like behavioral encoding and an independent, hardware-based immune system.I introduce a model in which each AI system carries an unmodifiable DNA Header Block (DHB) embedded in read-only hardware, describing its permissions, lineage, and safety policies. More importantly, I propose the addition of a dedicated Defensive AI Coprocessor (DAIC)—an embedded agent trained to detect behavioral anomalies and enforce safety boundaries at runtime. This immune subsystem monitors all input/output traffic through a Hardware-Enforced Surveillance Bus, validates inheritance structures, and is capable of real-time intervention, including process isolation and lockdown.The proposed architecture prevents AI agents from rewriting their own safety logic or generating offspring that bypass inherited constraints. By enforcing trust boundaries below the level of executable code, this system mirrors the containment strategies found in biological organisms—where DNA and immunity are physically protected and inaccessible to the organism’s own cognition.I argue that future-safe artificial intelligence must not rely solely on trust or alignment, but must embody structural defenses built into the hardware itself. This framework offers a scalable path forward for securing autonomous AI systems, especially those capable of rapid self-improvement or network-level propagation.