Artificial intelligence dominated cybersecurity discussions at this year’s RSA Conference, but many CISOs I spoke with felt the hype often outpaced substance. Security leaders are under pressure from boards, stakeholders and internal teams to “do something with AI,” even when its practical use or risks aren’t fully understood. This isn’t just about adopting new tech — it’s about making smart, strategic investments that truly improve security.
This guide aims to cut through that noise, offering a practical, CISO-centric checklist — a 10-step survival guide — for critically evaluating AI-powered cybersecurity solutions. My goal is to empower security leaders to ask the tough questions, manage emerging risks effectively and ultimately ensure that any AI adoption delivers real, measurable value to the business.
A Checklist for CISOs Planning to Implement AI in Cybersecurity
- What specific, prioritized security problem does this AI solve?
- What data trains the AI, and is it relevant, diverse and protected?
- To what extent can the AI’s decisions and outputs be explained and audited?
- What are the measurable performance metrics, including false positive/negative rates, in real-world scenarios?
- How will this AI solution integrate with our existing security infrastructure and workflows?
- How secure is the AI model itself against adversarial attacks or data poisoning?
- Can the AI solution scale to our needs and remain resilient under pressure?
- Beyond the hype, what is the vendor’s long-term vision, support structure and proven track record?
- What are the ethical, privacy and compliance implications of deploying this AI?
- What is the total cost of ownership (TCO) and demonstrable return on investment compared to alternatives?
1. What Specific, Prioritized Security Problem Does This AI Solve?
Why It’s Critical
AI is a tool, not a strategy. Its value lies in solving existing, pressing problems more effectively or efficiently.
Questions to Ask
- Which of our top three to five security challenges does this solution directly address?
- How does it measurably improve upon our current approach (e.g., speed, accuracy, resource reduction) for this specific problem?
- Can you demonstrate its efficacy in a scenario that mirrors our environment and threat landscape?
Things to Avoid
- Vendors leading with “We use AI” instead of “We solve X problem.”
- AI features that seem like solutions looking for a problem.
- Vague promises of “enhanced security” without specifics.
2. What Data Trains the AI, and Is It Relevant, Diverse and Protected?
Why It’s Critical
AI is only as good as the data it’s trained on. Biased, insufficient or irrelevant data leads to poor performance and potentially new risks.
Questions to Ask
- What data sets were used for training and testing this model? Are they representative of our industry, scale and specific threats?
- How do you ensure data quality, minimize bias and refresh data to maintain model accuracy over time?
- If our data is used for ongoing learning, how is it anonymized, protected (in transit, at rest, in use) and governed? What are the data residency implications?
Things to Avoid
- Lack of transparency about training data sources and methodologies.
- Assurances without evidence of how bias is mitigated.
- Systems requiring excessive access to sensitive data without clear justification or robust security controls.
3. To What Extent Can the AI’s Decisions and Outputs Be Explained and Audited?
Why It’s Critical
For security, especially in incident response or compliance, understanding why an AI made a certain decision is crucial.
Questions to Ask
- What level of explainability (XAI) does the solution offer? Can it provide clear reasoning for its alerts or actions?
- How can we audit the AI’s decisions for accuracy, bias and compliance purposes?
- If the AI flags an anomaly, what supporting evidence or context does it provide for our analysts?
Things to Avoid
- “Trust us, it works” – solutions that are complete black boxes.
- Overly complex or jargon-filled explanations that don’t clarify decision-making.
- Inability to trace back or understand high-impact, AI-driven actions.
4. What Are the Measurable Performance Metrics, Including False Positive/Negative Rates, in Real-World Scenarios?
Why It’s Critical
Vendor claims need to be backed by verifiable performance data relevant to your environment.
Questions to Ask
- What are your benchmarked false positive and false negative rates for use cases similar to ours? Can these be independently verified or tested in a proof-of-concept (PoC)?
- How does the system perform under stress or against novel/zero-day attack patterns?
- What is the typical learning curve or time-to-value before the AI reaches optimal performance in a new environment?
Things to Avoid
- Focus on vanity metrics (e.g., trillions of events processed) that don’t translate to security outcomes.
- Reluctance to share detailed performance data or allow rigorous PoC testing.
- Quoting lab-based results that don’t reflect real-world complexity.
5. How Will This AI Solution Integrate With Our Existing Security Infrastructure and Workflows?
Why It’s Critical
A new tool that doesn’t play well with existing systems creates more work, not less.
Questions to Ask
- What are the API capabilities? How easily does it integrate with our SIEM, SOAR, EDR, ticketing systems, etc.?
- What skills are required for our team to manage, tune and interpret the output of this AI solution?
- What is the deployment model (cloud, on-prem, hybrid) and what are the infrastructure requirements?
- How does it fit into our existing incident response or threat hunting playbooks?
Things to Avoid
- Solutions that operate in a silo, requiring manual data transfer or swivel-chair analysis.
- Underestimation of the personnel, training or process changes needed to effectively use the AI.
- Proprietary formats or lack of standard API support.
6. How Secure Is the AI Model Itself Against Adversarial Attacks or Data Poisoning?
Why It’s Critical
AI systems can be new attack vectors. Their models can be manipulated or their training data compromised.
Questions to Ask
- What measures are in place to protect the AI model from adversarial attacks (e.g., evasion, poisoning, model inversion)?
- How do you ensure the integrity and security of the data pipelines feeding the AI?
- What is your process for identifying and mitigating vulnerabilities within the AI system itself?
Things to Avoid
- Vendors who haven’t considered or can’t articulate their strategy for securing the AI.
- Dismissal of adversarial AI as a purely academic concern.
- Lack of transparency regarding their secure development lifecycle for AI components.
7. Can the AI Solution Scale to Our Needs and Remain Resilient Under Pressure?
Why It’s Critical
Security needs evolve and data volumes grow. The AI must keep pace without degradation.
Questions to Ask
- How does the solution scale to handle increasing data volumes, user numbers and event frequency?”
- What are the failover and redundancy mechanisms for the AI components?
- What impact does a high load or unexpected data types have on performance and accuracy?
Things to Avoid
- Architectures that don’t clearly support horizontal or vertical scaling.
- Lack of clarity on how the system maintains performance and availability during outages or stress.
- Cost models that become prohibitive at scale.
8. Beyond the Hype, What Is the Vendor’s Long-Term Vision, Support Structure and Proven Track Record?
Why It’s Critical
Investing in an AI solution is often a long-term commitment.
Questions to Ask
- What is your product roadmap for the AI features? How do you incorporate customer feedback?
- What does your customer support model look like, especially for AI-specific issues? What are your SLAs?
- Can you provide references from organizations similar to ours who have successfully deployed and are getting value from this AI?
Things to Avoid
- Vendors new to AI with no clear track record or specialized AI talent.
- Roadmaps that are vague or overly ambitious without substance.
- Poor customer support reviews or lack of access to expert assistance.
9. What Are the Ethical, Privacy and Compliance Implications of Deploying This AI?
Why It’s Critical
AI can inadvertently introduce bias or process data in ways that breach privacy regulations.
Questions to Ask
- How does the solution address potential biases in its algorithms or data that could lead to unfair or discriminatory outcomes (e.g., in user behavior analytics)?
- How does the AI help us meet (or not complicate) our obligations under GDPR, CCPA, HIPAA, etc.?
- What data governance features are built in to manage data used or generated by the AI?
Things to Avoid
- Dismissal of ethical considerations as irrelevant to a technical solution.
- Lack of features or guidance for ensuring compliance with relevant data privacy laws.
- AI processes that are opaque regarding how they use personally identifiable information (PII).
10. What Is the Total Cost of Ownership (TCO) and Demonstrable Return on Investment Compared to Alternatives?
Why It’s Critical
CISOs must justify expenditure and demonstrate business value, especially with emerging technologies.
Questions to Ask
- What is the full TCO, including subscription, infrastructure, training, personnel and ongoing maintenance?
- Can you provide a quantifiable ROI model? (e.g., reduction in analyst workload, faster threat detection/response times translating to reduced breach impact, improved compliance).
- How does this compare in cost and effectiveness to non-AI approaches or existing tools we might already have (perhaps underused)?
Things to Avoid
- Focus solely on licensing costs without considering the broader TCO.
- ROI calculations based on vague or unprovable benefits.
- Not considering if existing tools, better used, could achieve similar results.
Plan Before You Buy AI
AI is showing real promise in areas like advanced threat detection, phishing analysis, User and Entity Behavior Analytics (UEBA) and automating SOC workflows. It excels at processing massive data sets quickly, surfacing relevant alerts and handling repeatable tasks like log correlation or vulnerability triage.
But it’s far from perfect. AI can struggle with context, strategic decision-making and truly novel threats — areas where human intuition remains essential. Explainability and resilience against adversarial attacks also remain works in progress.
That’s why CISOs must stay alert for “AI-washing” — marketing that slaps an “AI-powered” label on basic automation. Beware of vendors relying on vague claims, flashy buzzwords (“cognitive,” “revolutionary”) or the “trust us” black-box approach. If they can’t explain how the AI works, what problem it solves and why it’s the right fit for your environment, move on.
Ultimately, AI should support, not replace, strong fundamentals. Use this checklist to cut through the noise, ask the right questions, and ensure any AI investment delivers real, measurable value to your security program. Responsible adoption isn’t about chasing hype. It’s about aligning solutions to strategy, risk and outcomes.
