2025年威胁情报领域人工智能与自动化状况报告_43页_27mb

报告摘要

AI and automation are extensively used in threat intelligence, with nearly 85% of organizations moving from pilots to active implementation.
A gap exists between AI feature availability and actual usage, showing a need for skills and trust development.
Security teams primarily seek efficiency gains but are slow to integrate these capabilities due to concerns about control, expertise, and compliance.
Overall, AI and automation are recognized as essential for enhancing defense capabilities against cyber threats.

AI and automation are now integral to threat intelligence workflows, transforming how organizations manage threats and stay proactive.
For instance, Recorded Future's platform automated reporting tasks, boosting efficiency by nearly 50th percent and reducing operational burdens.
Benefits include better alignment between threat intelligence and response times (45%), improved consistency in data processing (44%), and increased capacity to handle larger data volumes (43%).

Organizations report high satisfaction with AI adoption, with 87% noting met or exceeded expectations.
Key advancements include faster threat report generation and improved analyst efficiency, which can reduce workloads and response times.
For GenAI, features like automated summarization, multilingual translation, and contextual enrichment are key for trust and control, though some are lower priority for integration and testing.

Major barriers to expanding AI usage include limited internal knowledge (16%), budget constraints (14%), and concerns over compliance (14%).
Despite high trust in AI outputs, concerns about losing analyst control persist, highlighting the need for transparency and auditability in AI systems.
Smaller organizations more often report AI successes due to a more measured approach in larger enterprises.

Integrating AI into security tools can automate workflows, reduce investigation times by up to 50%, and improve overall threat intelligence effectiveness.
GenAI capabilities such as proactive detection and clear explanations are critical for organizational adoption and support decision-making across teams.
Reduced costs from breaches, enhanced detection rates, and streamlined workflows form a strong business case for AI implementation.

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