Automated Threat Hunting Using AI: AI-Driven Defence Strategy

Abstract: The exponential digitization of global infrastructures, coupled with the proliferation of cloud computing, Internet of Things (IoT) devices, and decentralized workforces, has inadvertently created a vast and highly vulnerable cyber attack surface. Concurrently, adversarial tactics have evolved with alarming sophistication. Modern cybercriminals and state-sponsored actors frequently deploy zero-day exploits, advanced persistent threats (APTs), fileless malware, and polymorphic ransomware that are expressly designed to circumvent traditional, perimeter-based security architectures [1], [2]. Historically, cybersecurity has relied heavily on reactive paradigms—such as signature-based Intrusion Detection Systems (IDS) and standard firewalls—which require prior knowledge of an attack vector to mount a defense. This reactive posture is fundamentally insufficient in an era where the velocity and novelty of cyber threats outpace human response capabilities. To neutralize these stealthy incursions, the cybersecurity industry must pivot toward proactive threat hunting: the iterative, aggressive process of searching through networks, endpoints, and datasets to uncover latent malicious activities that have successfully evaded initial automated defenses.

However, the sheer volume and complex dimensionality of telemetry data generated by modern IT ecosystems render manual threat hunting physically impossible and highly susceptible to analyst burnout and alert fatigue. This paper presents a comprehensive framework for Automated Threat Hunting driven by Artificial Intelligence (AI) and Machine Learning (ML), positioning it as the indispensable core of modern cyber defense strategies [2], [3]. By integrating AI into Security Operations Centers (SOCs), organizations can transcend the limitations of human capacity. This research explores the deployment of advanced AI mechanisms, specifically focusing on User and Entity Behavior Analytics (UEBA) for establishing baseline operational norms, deep learning neural networks for structural payload analysis without relying on known signatures, and natural language processing (NLP) to autonomously ingest and correlate global threat intelligence feeds [6], [9].

Furthermore, this document examines how AI-driven systems leverage continuous contextual analysis to connect seemingly disparate, low-level alerts across vast network topologies, unearthing coordinated, slow-moving attacks before data exfiltration or encryption occurs. We also detail the integration of AI with Security Orchestration, Automation, and Response (SOAR) platforms to execute instantaneous, autonomous remediation protocols, drastically reducing both the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) [10]. Finally, this paper critically assesses the practical implementation challenges—including high false-positive rates, data privacy constraints, and the emergence of adversarial AI [7], [11]—while forecasting the future scope of fully autonomous, self-healing networks governed by federated machine learning models [14].

Keywords: Automated Threat Hunting, Artificial Intelligence, Cybersecurity, Machine Learning, User and Entity Behavior Analytics (UEBA), Proactive Defense, Advanced Persistent Threats (APTs), Security Orchestration and Automation (SOAR), Anomaly Detection.