AI Training · Cybersecurity
AI Training for Cybersecurity & SOC Teams
SOC analysts and incident responders write almost as much as they investigate. Alert notes, shift handoffs, incident write-ups, playbook documentation, threat-intel summaries — it adds up fast, and this training helps a cybersecurity or SOC team get through that pile faster.
It stays deliberately narrow: the knowledge-work layer around security operations, not the operations themselves. No model gets to decide that an alert is real, own an incident, or take a blocking or isolating action — those calls stay with the analyst who's accountable for them.
- modules
- 6
- hours
- 13
Some of the 500+ brands we've worked with
Selected brands we’ve worked with across industries.
MeteksanGlobalCase Study
VisperaGlobalCase Study
Why this training
Why AI for this team?
A SOC runs under one rule that never gets negotiated away. The tooling can draft and summarize. It can suggest too. It cannot decide that an alert is real, own an incident, or take an action that blocks, isolates, or remediates anything on its own. That line exists before AI enters the picture, and this training keeps it exactly where it sits.
Analysts still spend much of the day writing around that judgment — alert notes, handoffs, incident timelines, and threat-intel summaries — where weak AI use can hide a real signal, invent a root cause, or drift a playbook from practice, and logs or case details can land in an ungoverned consumer tool. This training covers alert triage, incident documentation, detection-rule and playbook write-ups, and phishing/threat-intel summaries on your SIEM and ticketing conventions, skipping automated detection, automated response, and production detection code — teams building that tooling should see our Vibe Coding training; the broader AI training portfolio covers neighboring roles.
More alerts than analysts
AI clusters related alerts into a reviewable shortlist, but the analyst who owns the queue still decides to escalate, close, or investigate.
The paperwork outlives the incident
AI turns scattered incident notes into a timeline and first draft; the response lead verifies facts, scope, and severity before publishing.
Who still remembers why that detection rule exists?
AI explains a rule or playbook step in plain language, but detection engineers retain its logic and sign off on every change.
Don't let the phishing queue read itself
AI-assisted phishing and threat-feed summaries speed review, but a human analyst owns the verdict and every action that follows.
A phishing example loses its punch about a year in
AI drafts fresh phishing scenarios faster, but the security team reviews every piece before it reaches an employee inbox.
Syllabus
Training syllabus
Generative AI foundations for security operations
120 minBeginner
A realistic picture of what a language model can support inside a SOC, and where analyst judgment still has to make the call. Participants sort the day's tasks by risk and sensitivity. They also check how much context the model has before anyone touches a tool.
- How a language model generates and transforms security-related text
- What AI can support, and which decisions an analyst must keep
- Hallucinations and stale threat data, including a confidently wrong answer
- Classifying a SOC task by risk and sensitivity, with available context checked
- A pre-publish review gate
Alert triage and shift handoff support
120 minIntermediate
Practice using AI to make sense of a noisy alert queue and hand off a clean shift to the next team, with escalation, closure, and investigation calls staying with the analyst at every step.
- A high-volume alert queue, summarized into a reviewable shortlist
- Clustering related alerts without auto-closing or auto-escalating anything
- A shift handoff note, drafted from ticket and alert context
- Flagging evidence gaps early
- Why escalation and closure decisions stay with the analyst, always
Incident and post-incident documentation
150 minIntermediate
Turn an incident timeline and a pile of analyst notes into something reviewable. That starts with a live incident log. It ends with a post-incident report leadership can act on.
- A live incident timeline
- Post-incident write-ups: root cause and impact, anchored to the timeline
- Summarizing an incident for a non-technical executive audience
- A draft, checked for source-appropriate detail and accuracy
- Turning a lesson learned into a concrete follow-up action
- Incident classification and severity calls, kept with the response lead
Detection-rule and playbook documentation drafting
120 minIntermediate
AI drafts the documentation layer around detection logic and response playbooks, descriptions, rationale, runbook steps, while detection engineers keep the rule logic itself and validate every change before deployment.
- A plain-language description of what an existing detection rule is meant to do
- Playbook steps: triggers and actions, with escalation paths attached
- A runbook, structured so a new analyst can follow it under pressure
- Finding the gap between documented steps and what actually happens
- Reviewing AI-drafted documentation against the rule engineer's actual intent
Phishing report and threat-intel summarization support
120 minIntermediate
AI supports two high-volume review queues: user-reported phishing and external threat intelligence. It never replaces the analyst in either. The verdict stays with that analyst. So does any action that follows it.
- A reported email's headers, links, and content, summarized for review
- A preliminary phishing assessment
- Threat-intel bulletins, summarized into one digestible internal note
- Cross-referencing an intel summary against known exposure without auto-blocking anything
- Recognizing when a report needs escalation beyond AI-assisted review
Responsible adoption and SOC workflow design
150 minAdvanced
Set data-handling boundaries for logs, IOCs, and case data, choose approved tools, and design security-awareness content and a repeatable SOC workflow. Named human ownership sits at every review point.
- Log, IOC, case, and customer data, classified before it reaches a model
- Safe use of public, internal, confidential, and regulated security data
- Consumer versus enterprise tools, and the data-handling controls that follow
- Phishing examples and awareness copy, drafted for review before employees see them
- Mapping human review into triage and documentation, including intel workflows
- Choosing a measurable pilot
Outcomes
Outcomes & audience
What you will learn
- A shortlist that keeps the escalation call with the analyst
- Draft a shift handoff fast
- Incident notes become one report
- Brief a non-technical executive on an incident in one page
- Write up what a detection rule is supposed to catch
- Draft a phishing verdict for review
- Turn a threat-intel bulletin into one internal note
- Classify logs and IOCs, plus case data, before any model sees them
Who should attend
- Security-operations-center analysts, Tier 1 through Tier 3
- Incident responders and threat-intelligence analysts
- Detection engineers and security-engineering teams
- SOC managers and security-operations leads
- Security-awareness and training teams
- CISOs and security leaders overseeing AI adoption in the SOC
Format
Training format
Two days of instructor-led practice on realistic alert queues, incident scenarios, and playbook excerpts, splittable into half-day sessions and tailored to your SIEM, ticketing system, and approved tools.
- Format
- Onsite or live online
- Duration
- 2 days (about 13 hours, can be split into half-day sessions)
- Group size
- Up to 20 participants per group
- Language
- English or Turkish
- Materials
- Prompt library, workflow templates, and practice scenarios
- Certificate
- Certificate of completion
Keep exploring
Related programs
Next step
Scope the SOC program in one call
Questions
Frequently asked questions
Is this just for the SOC, or wider?
Security-operations roles and the teams around them: SOC analysts across every tier, incident responders, threat-intel analysts, detection engineers, SOC managers, and security-awareness teams. We can group a cohort by role so the scenarios match what each team does day to day.
Do participants need coding or detection-engineering skills?
Familiarity with your own alert queues, incident process, and documentation habits matters more than coding ability, though analysts who already write detection rules or response scripts get more out of pairing this with our Vibe Coding training.
Does this cover automated detection or automated response?
This program stays deliberately in the knowledge-work layer around security operations: triage support, documentation, summarization. Autonomous detection stays governed by your existing tooling and analyst judgment. Automated blocking does too, along with any action that isolates or remediates something.
Can this run online, and how long is it?
Two days, about 13 hours total. It runs onsite or live online and splits into half-day sessions to fit shift schedules and on-call rotations.
Can you build the syllabus around our SIEM and playbook format?
Yes. Before the training we go through your alert sources, ticketing system, playbook format, and escalation paths, then build exercises and templates from sanitized or synthetic scenarios, keeping live case data out of the room.
How does pricing work for a SOC program like this?
Group size, delivery format, customization depth, and location set the number. Tell us your team structure and goals and we'll build a proposal around them.
What do participants keep after the sessions end?
The prompt library, workflow templates, documentation scenarios, and pilot plan built during the sessions stay with the team. Nobody has to start from a blank page rebuilding this on day two. We're available for follow-up questions once the sessions wrap. An optional refresher or advanced workshop can be scheduled later, once adoption has had time to show where the gaps actually are.
How do you keep security data safe during training?
Logs and IOCs enter these exercises only as sanitized or fully synthetic material. The same rule covers case data. Participants practice classifying it and working inside your approved-tool policy, with human review over every triage, documentation, or intel-summary output the session produces.