Autonomous Threat Hunting and the role of artificial intelligence (AI)
Muhammad Sohail Sardar
Senior Security Analyst
Introduction
The escalating complexity and frequency of cyber threats necessitate a more proactive stance in cybersecurity defense mechanisms. The dynamic nature of cyber threats requires continuous monitoring and analysis, a task that surpasses human capabilities alone. Crucial aspect motivating the adoption of autonomous threat hunting is the imperative need to minimize response times in cyber incidents. It involves the utilization of advanced technologies like machine learning, artificial intelligence (AI), and big data analytics to detect, analyze, and respond to potential security incidents across diverse networks and systems.
Threat Evolution:
Cyber threat are continuously evolving which implies that "cybersecurity never sleeps". By the end of 2025, companies are still struggling with defending against AI generated attacks. This is because of employee training and lack of resources with traditional means of defense without the involvement of artificial intelligence (AI).
Modern & Concise
To tackle such challenges, threat hunting plays a critical role in modern cybersecurity era. Suspicious activities and cyber threats which evade existing security solutions, need to detect and remediate in real time scenarios. Due to the data volume and complexity, it is the need of the world to use advanced analytical techniques. A proactive stance allows organizations to get ahead of threats, reducing the risk of major breaches before they can develop into full-scale attacks.
Foundation for threat hunting:
Frameworks and standards from leading cybersecurity organizations are guiding industries across the spectrum to increasingly adopt threat hunting. Driven by established cybersecurity frameworks, threat hunting is seeing increased adoption across diverse industries. Attackers often retain the advantage of lateral movement, as it remains a primary blind spot for many security models. The identification of malicious activity is severely complicated when attackers leverage legitimate applications, blurring the line between normal and malicious behavior. The blind spots of traditional security tools are consequently exploited by threats, allowing them to persist undetected. Innovative approaches are essential for detecting these stealthy maneuvers, given the dual challenges of complex IT environments and sophisticated attack techniques.
More Active & Imperative
In threat hunting, identifying potential security breaches depends heavily on one crucial aspect: the differentiation and understanding of threat indicators. The Detection Maturity Level (DML) model values high-level, contextual indicators (goals, strategies, TTPs) over low-level data points (IPs, network artifacts), emphasizing that not all indicators are equally valuable. The key distinction lies in timing: IoCs (e.g., unusual network traffic) are reactive indicators of a breach, whereas Indicators of Concern, sourced from open-source intelligence, enable proactive hunting for emerging threats. The critical role of intelligence and proactive measures in modern cybersecurity is underscored by this distinction, which reveals the spectrum of approaches available for identifying and responding to threats.
Cyber Hunting Maturity Scale
The Threat Hunting Maturity Model defines five levels of proficiency, categorizing organizations by their threat hunting capabilities which are:
Initial (Level 0): The current system relies solely on automated reporting, with little to no routine data collection.
Minimal (Level 1):The process incorporates threat intelligence searches and maintains a moderate to high volume of data collection
Procedural (Level 2):The capability exists to execute third-party analysis procedures, which is facilitated by a high to very high degree of data collection.
Innovative (Level 3): Analysts at this level develop novel data analysis procedures, a process enabled by their high to very high data collection.
Leading (Level 4): The organization achieves high efficiency by automating proven data analysis procedures, leveraging a high to very high level of data collection.
2C’s in Thread hunting: Classic vs. Cutting-Edge
Initially, threat hunting was a predominantly manual endeavor. Security analysts leveraged their deep knowledge of network architecture and behavior to analyze data streams and construct hypotheses about potential cyber threats. To conduct these investigations, analysts manually correlated data from various platforms to identify patterns indicative of lateral movement and other sophisticated attacks. Manual investigations hunted for signs of lateral movement and other complex attacks by piecing together data from various sources. This new paradigm surpasses human limits, allowing defenders to leverage ML models for analyzing data at unprecedented scale and speed. The adoption of self-learning AI technologies (e.g., ML, DL, genetic algorithms) has a significant unintended consequence: the streamlining of cybercrime. Although these systems are engineered to defend by detecting anomalies and IoCs, they simultaneously provide attackers with powerful tools for automating and enhancing their offensive operations.
Monolithic vs. Agile
The integration of AI is revolutionizing cybersecurity, providing a critical advantage through its speed and precision in analyzing vast data. This capability is essential for defending against today's sophisticated threats in an era of explosive data growth. AI precision provides a critical advantage against increasingly sophisticated threats in an era of data explosion.
New Era of Threat hunting
Cybersecurity research is pushing its frontier by pursuing systems that can hunt threats autonomously, learning and adapting to new tactics without being pre-programmed with specific knowledge. A primary goal in cutting-edge cybersecurity research is to create autonomous threat hunting systems that can independently adapt to evolving attack tactics. A robust hypothesis development process and the ability to learn from diverse adversarial documentation are fundamental to such a system. This approach is vital to evolving threat detection and fortifying our defenses against the coming wave of cyber threats.
Key challenges
Threat hunting faces two core challenges: its procedures remain underdeveloped, and its integration within organizational structures is often vague. Both attackers and defenders are driving a strategic shift towards the integration of automation and machine-assisted tools.
Specialists also face several major challenges in threat hunting, such as: adversarial techniques, a lack of labeled data, multiple sources of log data, imbalanced datasets, a scarcity of human experts and data intelligence.
Conclusion
The integration of AI in cybersecurity represents an evolution of the long-standing practice of human-AI collaboration, which remains critically important. This is because, as established, even the most powerful AI systems cannot fully replace human analysts; they possess inherent limitations and lack the capacity for contextual understanding. Ultimately, the synergy between AI and human analysts will empower threat hunting, resulting in dynamic and highly effective defenses. Therefore, the priority must be to establish governance frameworks that balance security with innovation. By creating policies that ensure the responsible and transparent use of AI, we can secure data against misuse while still supporting the creative insights necessary for advanced cybersecurity. The challenges posed by AI in cybersecurity are not dead ends but solvable problems, demanding a collective response: the creation and adoption of universal ethical standards. The future of AI in threat hunting is promising; if developed with security and foresight, its enormous capabilities will be instrumental in helping organizations avert cyber threats.
Muhammad Sohail Sardar
Senior Security Analyst
Orange Cyberdefense China