Artificial intelligence is becoming a competitive differentiator for enterprise security teams. Yet, many CISOs remain cautious. The concern is understandable. The risk of exposing confidential data to external AI models, the uncertainty of regulatory expectations, and the potential for hallucinations make it difficult to approve broad AI adoption. 

In a WEI Tech Talk discussion with WEI Cybersecurity Solutions Architect Shawn Murphy, Cribl CISO Myke Lyons described how many CISOs are simply “shutting the door on AI” out of fear of data leakage and confidentiality threats. The challenge is that adversaries do not share these concerns. Attackers are already using AI tools aggressively, with no legal or governance constraints guiding their decisions. Ignoring AI does not create safety. It creates a widening asymmetry. 

Fortunately, CISOs do not need a complete enterprise AI program to begin realizing value. There is a practical starting point that delivers operational gains with near zero exposure. The most effective path forward is to focus on low risk, high return AI use cases. These are use cases that require no sensitive data, operate under human supervision, and strengthen SOC performance without introducing new pathways for loss. 

This article outlines four such starter use cases, explains why they are safe, and provides an actionable roadmap for CISOs who want measurable outcomes without compromising governance. 

Why Starting Small Is the Right Strategy 

CISOs face a deeply inconsistent landscape. On one hand, business leaders advocate for rapid AI adoption. On the other, security teams cannot ignore confidentiality and compliance obligations. Lyons notes that if he attempted to “pull the brake on all AI technologies,” he would simply leave the problem for the next CISO. The business expects progress. Executives expect clarity while boards expect a plan. What to do? 

Starting small aligns with the realities of enterprise governance. It allows teams to test AI capabilities in low risk domains, build internal muscle memory, and develop guardrails before scaling. Most importantly, it avoids the dangerous assumption that AI adoption requires perfect readiness. 

CISOs should look for entry points that meet the following criteria: 

• No regulated or sensitive data is processed. 

• AI outputs are advisory only. 

• Human review remains mandatory. 

• Workflows rely on metadata or natural language prompts rather than logs or customer data. 

• The model has no ability to take direct action against production systems. 

Use Case 1: AI Generated SIEM Queries That Accelerate Triage 

Writing SIEM queries is a persistent efficiency problem. Analysts often know the investigative question they want to ask but lack the fluency to translate it into KQL or proprietary syntax. Lyons recounted watching two analysts waste significant time banging out queries while a senior colleague coached them through each line. Their challenge was not analysis. It was syntax. 

AI eliminates this bottleneck without interacting with sensitive data. Analysts simply describe what they hope to find. The model produces a structured query they can validate and run. Because no logs are sent to the model, the data exposure risk is negligible. 

For CISOs, the value equation is compelling: faster triage, more consistent queries, and reduced training burden for junior staff. And no need to modify existing log flows or SIEM ingestion policies. For many enterprises, this use case can be adopted immediately. 

Use Case 2: AI as a Knowledge Sherpa for Internal Documentation 

A common SOC problem is the time lost searching Confluence, Jira, wikis, and ownership charts to understand an alert. Lyons described the ideal scenario. First, an alert fires. The AI immediately recognizes the application, summarizes its purpose, identifies the system owner, provides a location or business context, and presents the analyst with clarity that previously required tribal knowledge. 

This use case is low risk because it relies entirely on internal documentation. The model is pointed only at text repositories the organization already controls. There is no ingestion of logs, payloads, or regulated data. Access can be restricted to on-prem or isolated AI models, as Cribl has done, further reducing confidentiality exposure. 

For CISOs, the operational payoff is clear. The SOC becomes less dependent on hero analysts who carry undocumented institutional memory. Investigations become repeatable and auditable. New analysts become productive more quickly. And the organization retains knowledge that previously left with departing employees. 

Use Case 3: AI Supported Alert Contextualization Using Metadata Only 

Lyons highlighted an often overlooked insight. AI does not need raw data to provide meaningful support. Metadata alone can be highly powerful. Timestamps, hostnames, event categories, and source identifiers carry operational value while avoiding the sensitivity of full log payloads. Lyons explained that providing metadata only can “produce reasonable things” without exposing business critical information. 

CISOs can use this approach to introduce AI into alert enrichment without processing, configuration details, or customer content. The SOC receives streamlined contextual summaries, pattern comparisons, or priority hints while preserving data governance boundaries. 

This becomes particularly helpful in high volume environments where analysts face alert overload. AI can reduce the cognitive load without increasing risk. 

Use Case 4: AI Generated Case Summaries That Improve Investigation Consistency 

Lyons described how Cribl uses AI for a human in the loop case evaluation process. When the AI generates an investigation ticket, analysts review its accuracy. This creates a feedback loop that improves models over time while retaining human oversight. 

Case summarization is a low-risk domain because it involves small text fragments rather than full event streams. These summaries provide clarity, consistency, and time savings for SOC teams who struggle to document investigations amid high alert volumes. 

For CISOs, this also strengthens audit posture. More consistent case notes refine incident timelines, improve SOC reproducibility, and support compliance evidence without altering investigative workflows. 

What CISOs Should Avoid When Deploying Early AI 

The podcast also identifies several mistakes to avoid during early adoption. These common missteps serve as another example of why humans will always have a place in cybersecurity: 

• Do not allow AI to execute changes against production systems. Lyons is explicit that he will not use AI to block traffic, modify ports, or change configurations. 

• Do not point unrestricted AI models at full log stores. This creates unnecessary exposure. 

• Do not assume accuracy. Hallucination remains a material concern and require human review. 

• Do not deploy AI without policy guardrails, especially in environments with multi team access patterns. 

Choosing the Right Architecture for Low Risk AI 

Lyons referenced three architectural patterns that help CISOs adopt AI safely. 

• Self hosted or on prem models that process only internal documentation. 

• AI firewalls or policy gateways that enforce prompt controls and logging. 

• Metadata only enrichment flows that allow AI assistance without exposing raw events. 

WEI supports these adoption paths through SOC modernization engagements, cybersecurity assessments, and architecture advisory services. 

Closing Thoughts

Lyons shared a simple practice. Spend 15 minutes a day using AI. Familiarity reduces risk and prepares the organization for broader adoption. CISOs do not need enterprise scale models to begin. They need controlled use cases that improve outcomes without increasing exposure. Starting smaller is the safest way to move forward, and the organizations that take this path today will be the ones best positioned to secure their AI enabled future. 

Next Steps: Led by WEI’s cybersecurity experts and partnering with industry leaders, our cybersecurity assessments provide the insights needed to strengthen your defenses and ensure compliance. Whether you need to identify vulnerabilities, test your incident response capabilities, or develop a long-term security strategy, our team is here to help.

Contact WEI’s cybersecurity experts today to learn more about our assessments and discover how we can support your security goals. In the meantime, download our solution brief featuring WEI cybersecurity assessments.