Ethical Sinkholing in Autonomous Security Systems: A Software Architecture for Responsible AI-driven Threat Intelligence Gathering

The active implementation of autonomous security systems has revolutionized cyber defense since it allows collecting threat intelligence in real time and with the help of AI. One of these methods, sinkholing, i.e. redirecting malicious traffic to controlled conditions and observing it and mitigating it, has proved as a potent defense mechanism. Nevertheless, the conventional sinkholing activities pose profound ethical, legal, and governance issues, connected with intrusion into privacy, ownership of the data, proportionality, and collateral and unintended impact. This paper introduces an ethical sinkholing software architecture that is specifically targeted at autonomous security systems, and integrates a notion of responsible artificial intelligence directly into threat intelligence lifecycle. The suggested architecture combines explainable AI modules, policy aware decision engines, and human-in-the-loop supervision to make sure that sinkholing decisions are transparent, auditable, and comply with ethical and regulatory limitations. Such fundamental aspects as risk-based activation thresholds, privacy-sensitive data collection mechanisms, consent and jurisdiction-sensitive filtering, and ongoing monitoring of ethical compliance are also core aspects. The architecture allows responding adaptively to threats by decoupling detection, decision and intervention layers and reducing redundant data capture and mission creep. In addition, the framework aids accountability by the recording of immutable logs and a post-incident review that enables organizations to exhibit due diligence and responsible use of autonomous capabilities.