AI TRAINING
AI for Cybersecurity Operations
Equip SOC teams to deploy, tune, and defend AI-augmented threat detection and response workflows.
What it covers
This practitioner-level programme trains security operations professionals to integrate AI and machine learning into core cybersecurity workflows, including threat detection, phishing prevention, and automated incident response via SIEM/SOAR platforms. Participants work through hands-on labs simulating real attack scenarios and learn to identify adversarial AI threats such as prompt injection and model poisoning targeting internal AI systems. The format combines instructor-led sessions with live tooling exercises on platforms like Microsoft Sentinel, Splunk, and CrowdStrike. Graduates leave with a repeatable AI-augmented SOC playbook they can deploy within their organisation.
What you'll be able to do
- Configure an AI-based anomaly detection rule within a SIEM platform and explain the model's decision logic to stakeholders
- Build and test an automated SOAR playbook that triages phishing alerts using an NLP classifier
- Identify and document prompt injection vulnerabilities in an enterprise-facing AI assistant or copilot
- Conduct a red-team exercise simulating adversarial attacks against an AI-augmented SOC tool and propose mitigations
- Produce a governance checklist for deploying AI models in a regulated security operations environment
Topics covered
- AI-powered threat detection: supervised and unsupervised anomaly detection models
- SIEM/SOAR automation with AI: use cases in Microsoft Sentinel, Splunk SOAR, and Palo Alto XSOAR
- Phishing and social engineering prevention using NLP-based classifiers
- Adversarial AI threats: prompt injection, model poisoning, and data exfiltration via LLMs
- Securing internal AI systems and copilots deployed in enterprise environments
- Automated incident triage and response playbook design
- Threat intelligence enrichment using generative AI and LLM-based analysis
- Compliance and governance considerations for AI in security operations
Delivery
Delivered as a blended programme over three to five days on-site or via interactive remote sessions. Approximately 60% of time is hands-on lab work using pre-configured sandboxed environments replicating real SIEM/SOAR stacks. Participants receive a lab workbook, threat scenario library, and AI-augmented SOC playbook template. Remote delivery uses a dedicated virtual lab environment accessible throughout the programme. A follow-up Q&A session is typically offered four weeks post-training to review real-world implementation challenges.
What makes it work
- Involving SOC tier-1 and tier-2 analysts alongside security architects to ensure operational buy-in and practical relevance
- Running the training against the organisation's own (anonymised) log data and alert history to ground exercises in real context
- Establishing a post-training AI model review cadence so detection models are retrained as threat landscapes evolve
- Pairing training with a formal AI security policy update to embed new practices into SOC operating procedures
Common mistakes
- Deploying AI detection models without baseline tuning, leading to alert fatigue from high false-positive rates
- Overlooking adversarial threats specific to AI systems, leaving internal copilots and LLM integrations unaudited
- Treating SOAR automation as a black box without analyst understanding, causing misplaced trust in automated triage decisions
- Skipping governance and compliance review for AI tooling, creating liability under NIS2, GDPR, or sector-specific regulations
When NOT to take this
This programme is not the right fit for organisations that have not yet deployed a SIEM solution or established basic security monitoring practices — foundational security operations training should come first before introducing AI augmentation.
Providers to consider
Sources
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