#26: The Future Threat Hunter
Human Expertise in an Autonomous SOC
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Key takeaways
Future threat hunters will need skills in AI governance, model limitations, RAG, prompt engineering and agentic workflows alongside traditional cybersecurity expertise.
AI capabilities are shipping inside connected products faster than security programs can keep up. Model endpoints, inference APIs, and the pipelines behind them are now part of your product’s attack surface, and most testing programs still treat them as someone else’s problem. Winmill’s Penetration Testing Stream covers the full product ecosystem in one engagement: AI models and the APIs that expose them, adversarial and data poisoning threats, plus the devices, applications, cloud backend, and network they live in. Testing starts within days, with severity ranked findings delivered in a live portal. To see our approach firsthand, we are offering Cyber_AI readers a complimentary penetration test. Artificial intelligence has already transformed many aspects of cybersecurity, from malware detection to vulnerability management. Threat hunting is no exception. What began as simple machine learning models identifying anomalous behaviour has evolved into AI assistants capable of generating search queries, summarising investigations and recommending remediation actions. The next phase promises to be even more significant. Across the cybersecurity industry, vendors are introducing autonomous AI agents that can investigate alerts, correlate evidence across multiple systems and execute predefined response actions with minimal human intervention. At the same time, attackers are adopting many of the same technologies to automate reconnaissance, identify vulnerabilities and accelerate intrusion campaigns. As both defenders and adversaries embrace AI, threat hunting is entering a new era—one in which success depends less on manually searching log data and more on directing intelligent systems capable of analysing information at machine speed. Rather than replacing human threat hunters, this shift is redefining their role. From analyst to investigation managerTraditional threat hunting has always been highly manual. Analysts formulate hypotheses, write queries, gather telemetry, correlate evidence and determine whether suspicious activity represents a genuine compromise. Even with the AI capabilities discussed in the previous articles, humans still perform most of the investigative reasoning. Autonomous AI agents are beginning to change this workflow. Instead of asking an AI assistant to generate a query, analysts may assign an investigative objective. For example:
Rather than simply generating a search, the AI agent may independently:
Instead of directing every individual step, the analyst reviews the investigation, validates conclusions and decides whether further action is necessary. The role increasingly resembles supervising a team of junior investigators rather than conducting every task personally. The emergence of autonomous SOCsSecurity Operations Centres (SOCs) have traditionally relied on layers of automation. Security Orchestration, Automation and Response (SOAR) platforms already automate repetitive processes such as ticket creation, evidence collection and endpoint isolation. AI extends automation beyond predefined workflows. We’re no longer able to merely follow rigid playbooks as, now, AI systems increasingly make contextual decisions based on available evidence. An autonomous SOC may automatically determine:
These capabilities dramatically reduce investigation time while allowing analysts to focus on higher-value decisions. However, autonomy obviously also introduces new responsibilities. Security teams must define confidence thresholds, approval workflows and governance mechanisms to ensure automated investigations remain trustworthy and auditable. Human judgement becomes more valuableOne of the more surprising consequences of AI adoption is that human expertise becomes increasingly important rather than less. AI excels at recognising patterns across vast datasets, whereas humans excel at understanding context. An authentication event occurring at three o’clock in the morning may appear suspicious. For a multinational organisation supporting global customers, it may be entirely routine. Similarly, AI may identify administrative PowerShell activity that appears highly anomalous. A human analyst understands that the infrastructure team deployed emergency updates during the same period. Context transforms anomalies into explanations. Effective threat hunting therefore depends upon combining AI’s analytical speed with human understanding of business operations, organisational priorities and acceptable risk. AI versus AIDefenders are not the only ones adopting artificial intelligence. Threat actors increasingly employ AI throughout the attack lifecycle. Large language models assist with phishing campaigns, malicious scripting, vulnerability research and social engineering. Automated reconnaissance systems identify exposed services, prioritise vulnerable targets and adapt intrusion techniques more rapidly than traditional attack tooling. Future malware may incorporate AI-driven decision making, allowing malicious software to alter behaviour dynamically depending on the environment it encounters. Similarly, attacker infrastructure may automatically generate new phishing lures, modify command-and-control communications or identify opportunities for lateral movement without direct operator involvement. Threat hunters must therefore prepare to investigate attacks that evolve continuously rather than following predictable playbooks. This reinforces the importance of behavioural analysis over static signatures. The growing importance of data qualityAI systems are only as effective as the data they analyse. As organisations deploy increasingly autonomous hunting capabilities, telemetry quality becomes even more critical. Incomplete endpoint visibility, inconsistent timestamps, missing identity logs or poorly maintained asset inventories all reduce AI effectiveness. Successful organisations increasingly treat telemetry as strategic infrastructure rather than operational by-products. High-quality logging, consistent asset management and accurate identity information become competitive advantages. The future threat hunter will therefore spend less time collecting missing evidence and more time ensuring reliable data pipelines exist before investigations begin. Building trust in AI investigationsAs AI assumes greater responsibility, analysts must understand how conclusions were reached. Explainability becomes essential. If an AI agent recommends isolating a critical production server, security teams must understand the evidence supporting that recommendation. Modern AI platforms increasingly provide investigation graphs, evidence chains and references to individual log events that contributed to each conclusion. Rather than accepting opaque recommendations, analysts should be able to validate every investigative step. Trust develops through transparency rather than automation alone. This is particularly important in regulated industries where investigation decisions may require legal review, compliance reporting or forensic preservation. Skills for the next generation of threat huntersTechnical expertise remains fundamental, but the balance of skills is changing. Future threat hunters will still need to understand operating systems, networking, authentication, malware behaviour and attacker techniques. However, additional competencies are becoming increasingly valuable. Analysts will need to understand how large language models operate, recognise the limitations of machine learning, validate AI-generated conclusions and identify hallucinated or misleading responses. Knowledge of Retrieval-Augmented Generation (RAG), AI agents, model security and prompt engineering will increasingly complement traditional detection engineering skills. Equally important is the ability to ask effective investigative questions. As AI becomes more capable, the quality of analyst direction increasingly determines the quality of investigative outcomes. The best threat hunters may become those who know not only how attackers behave, but also how to guide AI systems towards discovering that behaviour efficiently. Governance and responsible adoptionDeploying AI within threat hunting introduces governance considerations extending beyond technical implementation. Organisations must determine:
And not necessarily in that order. Answering these questions requires collaboration between security teams, governance specialists, legal advisors and executive leadership. Responsible AI adoption depends as much upon organisational policy as technical capability. Preparing for continuous evolutionPerhaps the greatest challenge facing threat hunters is the pace of change. AI capabilities improve rapidly and attack techniques evolve continuously. Rather than mastering a fixed collection of tools, successful threat hunters must develop adaptable investigative thinking. Understanding attacker behaviour, asking meaningful questions, validating evidence and exercising sound judgement remain timeless skills even as technology changes around them. AI simply enables those skills to operate at greater scale. Looking aheadThreat hunting has always required curiosity, analytical thinking and persistence. Those qualities will remain indispensable as AI becomes embedded throughout the Security Operations Centre. The difference is that tomorrow’s threat hunters will spend less time manually querying logs and reconstructing attack timelines. Instead, they will supervise intelligent investigative systems capable of analysing millions of events, correlating evidence across diverse environments and proposing well-supported conclusions within minutes. This evolution does not diminish the role of the human analyst, of course. The future belongs to security professionals who understand both adversary behaviour and artificial intelligence—individuals who can combine machine-scale analysis with human reasoning to identify threats that neither humans nor AI could uncover alone. Threat hunting is no longer simply about finding attackers as it is becoming the discipline of directing intelligent systems to uncover what matters most. Further reading
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