AI has changed the tempo of cybersecurity.
For defenders, it can help detect threats faster, reduce repetitive work, and speed up response. For attackers, it can make phishing more convincing, social engineering easier to scale, and deepfakes harder to identify. The same technology helping security teams move faster is also giving adversaries new ways to move with more speed and precision.
AI fluency means knowing how AI reaches conclusions, where it can fail, how to validate its outputs, and when human judgment needs to take over. Without that understanding, AI can add uncertainty to an already pressured SOC.
The pressure is already visible. According to Market.Biz, an online market research and business intelligence platform, 87% of organizations have been targeted by an AI cyberattack in 2025, while deepfake attacks have risen by more than 2,000% since 2022. At the same time, ISC2’s 2024 cybersecurity workforce study reports 5.5 million cybersecurity professionals worldwide, with a workforce gap of 4.8 million.
For SOC teams, those numbers reflect a difficult reality. They are being asked to adopt AI, defend against AI-enabled threats, and prove that AI-assisted decisions are safe, explainable, and aligned to policy. Many organizations have already invested in AI-powered security tools. Fewer have built the fluency needed to use, govern, and trust them in daily operations.
AI fluency means knowing how AI reaches conclusions, where it can fail, how to validate its outputs, and when human judgment needs to take over. Without that understanding, AI can add uncertainty to an already pressured SOC.
AI Fluency Gap Is Now a Security Risk
Most security teams have deep experience with systems built on rules, signatures, thresholds, and deterministic logic. These methods still have value, especially for known patterns and repeatable workflows. AI-powered threats and attacks behave differently. They adapt quickly, mimic trusted communication, and exploit human behavior at scale.
AI adoption inside the SOC is moving quickly. Analysts may receive recommendations without enough context about confidence, source data, or reasoning. Incident responders may need to act on prioritized alerts without fully understanding how the system reached its conclusion. Security leaders may struggle to show that AI is operating within approved risk, privacy, and compliance boundaries.
The risk has farther implications. Poorly governed AI can introduce bias, misclassify threats, expose sensitive data, or create recommendations analysts cannot validate.
Closing the gap requires three core foundations: governance, transparency, and training.
Governance Creates Control
AI in the SOC depends on sensitive, high-volume security data. That data includes user behavior, identity signals, endpoint activity, network traffic, and incident history. Strong governance defines how and why that data is accessed, used, retained, and protected.
With proper governance, ownership remains clear. Security teams need to know who is accountable for AI models, AI-powered workflows, data usage, escalation rules, and response actions. Without clear ownership, AI creates new operational risk instead of reducing existing risk.
A strong governance model should include model ownership, role-based access controls, privacy safeguards, audit trails, escalation paths, and clear rules for human approval. Bias and fairness risks should be reviewed early. Sensitive data should be protected through controls such as PII filtering and policy-based access.
Governance keeps AI controlled, defensible, and useful under pressure.
Transparency Builds Analyst Trust
Analysts need to understand the reasoning behind recommendations. Transparent AI helps analysts see which data influenced a decision, why an alert was prioritized, and where uncertainty remains. That context improves investigation quality and reduces blind trust in automated outputs.
In daily SOC operations, transparency will affect speed and confidence. Analysts can validate AI-assisted findings faster when they can see the supporting evidence. Incident responders can make better containment decisions when they understand how risk was assessed. Security leaders can support compliance and board reporting when AI-assisted decisions are auditable.
With transparency, analysts spend less time chasing unclear recommendations when outputs include context, rationale, and evidence. Clear reasoning gives the team the confidence to accept, challenge, or escalate a decision.
Training Turns AI Into a SOC Capability
AI fluency needs to reach every part of the security organization. It cannot sit only with data scientists, engineers, or AI specialists.
Tier 1 analysts need to understand how AI affects alert triage, prioritization, and false positive reduction. Tier 2 analysts need to validate AI-assisted investigations, identify gaps in context, and challenge weak correlations. Incident responders need to understand how AI supports containment and response preparation. Security leaders need to assess governance, risk, productivity, and measurable outcomes.
Training should cover how predictive models work, how AI-generated outputs should be interpreted, how bias and false correlations appear, and how analysts can give clear instructions to AI systems. It should define when AI can assist, when human approval is required, and when a decision must be escalated.
Strongest programs are practical and role-based. Teams need hands-on experience using AI in realistic SOC workflows, including triage, investigation, threat hunting, response, reporting, and governance review.
Training is continuous. Threats will evolve. Models will change. Operating practices will mature. A one-time program will not keep pace with AI adoption or AI-enabled attacks.
Organizations that underinvest in training risk building dependency on systems their teams do not fully understand. That weakens trust, slows adoption, and limits the value AI can deliver.
Human Judgment Owns the Outcome
AI can help the SOC move faster. It can surface context, reduce repetitive work, recommend actions, and accelerate response. However, human analysts still own the outcome. A human-in-the-loop model keeps responsibility clear. Analysts validate decisions, manage exceptions, apply judgment, and take accountability for high-risk actions.
The organizations that succeed with AI in cybersecurity will build fluency around it. Governance creates control. Transparency builds trust. Training gives teams the skill to use AI with confidence.
These foundations turn AI from a promising tool into a disciplined SOC capability.
Guest author Zubair Chowgale is the Director – Sales Engineering (EMEA & APJ) at Securonix, an AI powered cybersecurity company transforming how security operations are delivered, measured, and scaled. Any opinions expressed in this article are strictly those of the author.
