Security operations centers (SOCs) must modernize, and many enterprise organizations are hoping artificial intelligence can help reduce noise, lessen alert fatigue, automate responses, and scale capacity for security analysts. However, even with the promise of AI, CISOs must conduct thorough evaluations to ensure the new technologies don’t introduce new risks. CISOs should consider two key areas: selecting the right types of AI for SOC workflows and establishing a framework for vetting those tools before deployment.

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How AI’s Role is Expanding in the SOC

Agentic AI, which uses specialized pieces of software that are capable of making decisions and interacting independently within environments, is emerging as a key opportunity to improve incident response workflows. By using agentic AI, these tools can automate first-level, or Tier 1, and, in some cases, Tier 2, SOC response functions. Security analysts using AI could perform faster incident triage and respond with greater efficiency to alerts. Still, AI tools also raise valid concerns around false positives and “hallucinations,” which can be AI false positives in a scenario in which AI generates incorrect information but presents it as fact.

Other categories of tools are also maturing. Workflow automation platforms bring AI closer to the data and enable flexible orchestration of SOC tasks. These approaches may be less risky than agentic AI, since workflows typically follow human-designed playbooks rather than autonomous decision-making.

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What is a Good Framework for AI Tool Evaluation

CISOs evaluating AI for SOC use should not treat these tools as a single category. Instead, a structured evaluation framework is essential. Recommendations include:

• Risk Classification: Assess tools based on data sensitivity, integration depth, access permissions, and potential for misuse. Assigning low/medium/high risk levels helps align tools with appropriate guardrails. For instance, an AI tool that can execute code may require much stricter controls than one analyzing public datasets.
  
  
• Vendor vs. Open-Source Trade-offs: Open-source models provide flexibility but often lack monitoring and guardrails, which increases operational overhead. Commercial offerings integrate built-in risk management and monitoring, lowering the cost of governance over time.
  
  
• Token Costs and Scalability: Large language models carry cost risks tied to token usage. Emerging “small language models” or specialized agents may offer more efficient, task-specific performance at lower cost.
  
• Cross-Team Governance: Input from compliance, legal, and privacy teams should shape vendor questionnaires and system reviews. Using model or system cards provides transparency into training data, safety evaluations, and performance metrics.

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What Does AI Mean for CISOs

CISOs face a balancing act: unlock AI’s potential to strengthen SOC efficiency, while maintaining oversight to avoid introducing new risks. The most effective path is likely a hybrid approach: deploying commercial platforms for core monitoring and guardrails, while selectively experimenting with open-source or niche AI tools for specialized use cases.

AI is not an instant remedy for all issues in the SOC, but with a structured evaluation framework and clear risk guardrails, it can deliver real value without amplifying exposure.

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