A3TH: An Adaptive AI-Driven Autonomous Threat Hunting Framework for Proactive Cyber Defense in Evolving Digital Environments
Annotatsiya
The vulnerabilities in this vigorous digital landscape are taking a more sophisticated shape as the nature and danger of cyber threats and the nature of cyber threats are taking new forms as a zero-day attack, polymorphic malware, insider threat and advanced social engineering methodologies and where traditional reactive security measures are no longer relevant. This issue of organizations detecting the threat, and that they need to mitigate the threat in real time is rather of a challenge in the light of the fact the behaviour of the adversaries is very much similar to that of legitimate user behaviour and as such will create ambiguity issues that will make an organization believe that it has hit a false positive or missed a threat. A proactive approach to cyber threat countering, the Adaptive AI-Driven Autonomous Threat Hunting (ADCH) Framework, is the focus of this paper as it will be able to monitor, analyse, and mitigate the development of the new threats far before it becomes reality. Behavioural profiling and reinforcement learning are employed at ADCH to differentiate between innocent and harmful actors on the fly even in the ambiguous or complex situation, and ethical precautions are taken so that the deepfake interaction modules are tightly regulated and privacy safeguarding. The framework brings together automated threat intelligence extraction where Indicators of compromise (IoCs) are extracted, classified and pooled across different sources and offer rapid and actionable information. Additionally, ADCH is compatible with Siem and SOAR, automates the incident response and mitigation process to minimise latency and dependency on people. Secure logging provides auditability resistant to tampering and transparent recording of events without de-anonymization of sensitive operational data, with blockchain used to enforce the use of permissioned ledger systems, smart contracts, and zero-knowledge proofs. The results of simulation testify that ADCH has been doing consistently well in terms of detection performance and accuracy, precision, recall and F1 scores, the results stand at $80-90$ and far better than the traditional threat hunting systems. Autonomous AI-controlled detection, ethical simulation, automated intelligence extraction, coordinated response, and blockchain-based logging can be combined to help in establishing a strong and intelligent paradigm of defense against the emergent and advanced cyberspace threats and ensure the safety of the digital infrastructures, transparency, accountability and ethical standards.