Future of Threat Intelligence GigaOm’s Howard Holton on Why AI Will Be the OS of Security Work
The cybersecurity industry has witnessed numerous technology waves, but AI's integration at RSA 2025 signals something different from past hype cycles. Howard Holton, Chief Technology Officer at GigaOm, observed AI adoption across virtually every vendor booth, yet argues this represents genuine transformation rather than superficial marketing. His analyst perspective, backed by GigaOm's practitioner-focused research approach, reveals why AI will become the foundational operating system of security work rather than just another tool in an already crowded stack.
Howard's insights challenge conventional thinking about human-machine collaboration in security operations. He explains how natural language understanding finally bridges the gap between human instruction variability and machine execution consistency, solving a problem that has limited automation effectiveness for decades. Howard also explores practical applications where AI handles repetitive security tasks that exhaust human analysts, while humans focus on curiosity-driven investigation and strategic analysis that machines cannot replicate.
Topics discussed:
- The fundamental differences between AI's practical applicability and blockchain's limited use cases, despite similar initial hype cycles and market positioning across cybersecurity vendors.
- How natural language understanding creates breakthrough human-machine collaboration by allowing AI systems to execute consistent tasks regardless of instruction variability from different analysts.
- The biological metaphor for human versus machine intelligence, where humans operate as "chaos machines" with independent processes driven by curiosity rather than single-objective optimization.
- GigaOm's practitioner-focused approach to security maturity modeling that measures actual organizational capability rather than vendor feature adoption or platform configuration levels.
- Why AI will become the operating system of security work, following the evolution from Microsoft Office to SaaS as foundational business operation layers.
- The strategic advantage of AI handling hyper-repetitive security processes that traditionally drive human analysts to inefficiency while preserving human focus for curiosity-driven investigation.
- How enterprise security teams can identify the optimal intersection between AI's computational strengths and human analytical capabilities within their specific organizational contexts and threat landscapes.
Key Takeaways:
- Evaluate your security maturity models to ensure they measure organizational capability and adaptability rather than vendor feature adoption or platform configuration levels.
- Identify repetitive security processes that exhaust human analysts and prioritize these for AI automation while preserving human focus for curiosity-driven investigation.
- Leverage natural language understanding in AI tools to standardize security process execution despite instruction variability from different team members.
- Audit your current technology stack to distinguish between genuinely applicable AI solutions and superficial AI marketing similar to the blockchain hype cycle.
- Create practitioner-focused assessment criteria when evaluating security vendors to ensure solutions address real-world enterprise implementation challenges.
- Develop language-agnostic security procedures that AI systems can interpret consistently regardless of how different analysts explain the same operational requirements.
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