From Manual Playbooks to AI Threat Hunting
Security operations teams are facing a fundamental speed problem: attacks unfold at machine pace while investigations still follow human timelines. Traditional workflows—alert triage, manual log review, and ticket-driven response—struggle against automated campaigns and agile threat actors. Vendors are responding by embedding threat detection AI into existing platforms, transforming how investigations start and how incidents are contained. AI threat hunting no longer sits at the experimental edge; it is being woven directly into managed detection and response services and threat intelligence tooling. The goal is not to replace analysts, but to offload repetitive tasks and accelerate pattern recognition across vast telemetry. As organisations contend with overlapping alerts, complex hybrid environments and expanding attack surfaces, security automation tools are becoming the practical way to keep pace, shrinking the gap between compromise, detection and response.
Prevyn AI: Agentic Threat Hunting at Machine Speed
Group-IB’s Prevyn AI illustrates how vendors are re-architecting platforms around AI-assisted threat hunting. Embedded as the cognitive core of the company’s Unified Risk Platform, Prevyn AI is available to existing Threat Intelligence and Managed XDR customers at no additional cost, signalling a shift toward AI as a built-in capability rather than a premium add-on. The system draws on Group-IB’s intelligence data lake, fed by cybercrime investigations and collaborations with law enforcement, to reason about attacker behaviour instead of relying primarily on open-source feeds. Within Threat Intelligence, Prevyn AI orchestrates 11 specialised agents spanning malware analysis, threat actor tracking and dark web monitoring, modelled on investigative logic from high-tech crime cases. Internal testing indicated more than a 20% uplift in research quality. In practice, this means earlier insight into attacker intent and infrastructure staging, helping defenders move from reactive containment to proactive, pre-emptive defence.
AI-Assisted Response with Human-in-the-Loop Governance
While automated cyber response is gaining momentum, governance concerns are pushing vendors toward human-in-the-loop designs. Prevyn AI’s Managed XDR capabilities focus on reducing manual workload, not bypassing human oversight. The platform analyses alerts, drafts incident reports and prepares structured remediation workflows, but every action requires analyst approval before execution. This design aligns with emerging regulatory frameworks such as the EU AI Act and sectoral governance standards, which emphasise accountability for automated security decisions. For security teams, this approach balances speed and control: AI accelerates evidence gathering, correlation and playbook preparation, while humans retain authority over containment steps that may impact production systems. The result is faster response without ceding critical decisions to opaque algorithms. As more organisations adopt threat detection AI and security automation tools, this model of assistive, supervised automation is becoming a template for compliant, trusted AI operations.
Unified Risk Views: Tech Mahindra and Cisco’s Cyber Resilience Fabric
AI-powered threat hunting is only as effective as the data context it can see. Tech Mahindra and Cisco’s Cyber Resilience Fabric tackles this by fusing Cisco’s Splunk Enterprise Security platform with Tech Mahindra’s Risk Scoring Platform into a single environment. The result is AI-assisted analytics that blend security events, operational metrics and contextual risk information, offering a unified view of cyber exposure. Instead of treating all alerts equally, the platform performs contextual risk prioritisation, ranking incidents by likely business impact. This reduces operational noise, improves triage accuracy and helps security leaders justify where to focus limited resources. In a landscape where overlapping tools generate duplicated or conflicting alerts, integrating detection, analytics and response into one risk-led decision layer is becoming essential. Customers gain earlier threat detection, faster, prioritised response and more structured recovery when critical services are disrupted.
AI Threat Hunting as a New Operational Baseline
Taken together, platforms like Prevyn AI and Cyber Resilience Fabric point to a new operational baseline for enterprise security. AI threat hunting and automated cyber response capabilities are being embedded into mainstream products, not reserved for highly specialised teams. Security leaders are looking for ways to connect AI analytics with governance, compliance and demonstrable business value. Unified visibility across alerts, risk scores and operational impact helps translate technical events into decisions executives can act on. At the same time, human oversight remains central, ensuring that AI-augmented workflows stay aligned with regulatory obligations and organisational risk appetite. As attackers continue to operate at machine speed, enterprises that rely solely on manual investigation will fall behind. Those that adopt AI-driven, risk-led security automation tools stand a better chance of reducing response times, improving detection accuracy and building sustainable cyber resilience.