Understand how AI supports modern cyber defence. Explore AI-assisted security monitoring, threat intelligence, anomaly detection, phishing analysis, log review, incident triage and the security risks of AI applications. Learn through guided explanations and controlled, authorised lab scenarios with human verification at every stage.
Course content: November 2026
01 · Understand the tools
02 · Work on practical briefs
03 · Review and improve
Guided learning at O7 Services.
Contact O7 Services to confirm current course duration, batch schedule, fees and tool availability.
AI can help security teams process large volumes of events, identify unusual patterns, enrich alerts, summarise threat intelligence and organise incident information. It supports analysts; it does not replace security controls, expertise or verification.
Defensive teams may use machine learning for anomaly detection and classification, and language models to summarise logs, draft incident reports or explain security concepts. Outputs can be incomplete or incorrect, so analysts must validate evidence before acting.
AI also creates new security risks. Applications that use language models can be exposed to prompt injection, sensitive-data disclosure, unsafe tool access and insecure integrations.
Organisations need people who understand both modern cyber threats and the safe use of AI tools in security operations. Combining security fundamentals with AI awareness helps learners evaluate automated findings and support defensive teams.
At O7 Services, the learning approach focuses on defensive concepts, guided demonstrations, controlled lab work and clear reporting. Practical activities must use systems and datasets where permission has been granted.
AI can strengthen selected security workflows, but it is not a substitute for established controls, skilled analysts or incident response procedures.
| Area | Traditional approach | AI-assisted support |
|---|---|---|
| Security monitoring | Analysts review events, alerts and dashboards | AI can help group events, summarise context and prioritise review |
| Threat intelligence | Teams collect and correlate indicators from trusted sources | AI can assist with summarisation, enrichment and reporting |
| Phishing analysis | Analysts inspect sender, links, language and attachments | AI can help classify suspicious patterns for human validation |
| Incident response | Teams follow documented playbooks and escalation paths | AI can help organise timelines and draft reports; response remains authorised and human-controlled |
| AI application security | Teams apply application security and access controls | Teams additionally assess prompt injection, data leakage and model/tool boundaries |
Learn the defensive use of AI alongside the security principles needed to evaluate and verify its output.
Explore monitoring, investigation, threat analysis and response workflows.
Work with sample logs, alerts and controlled learning scenarios.
Understand prompt injection, data leakage and unsafe integrations.
Emphasise authorisation, privacy, validation and documented procedures.
Learn through guided security examples and controlled exercises. Contact the training team for trainer details and current batch availability.
AI can make mistakes, miss threats or produce misleading explanations. Security professionals must validate evidence, follow approved procedures and retain responsibility for decisions.
The course emphasises safe, authorised learning environments and defensive application of AI rather than unauthorised access or harmful activity.
A defensive learning path covering security foundations, AI-assisted operations and the security of AI-enabled applications.
| Module | Topics | Practical outcome |
|---|---|---|
| 1. Cyber security foundations | Security principles, assets, threats, vulnerabilities, risk and defence in depth | Understand the defensive security lifecycle |
| 2. AI and machine learning basics | Classification, anomaly detection, model limitations and false positives | Understand where AI can support security work |
| 3. Security operations and SOC | Events, alerts, triage, escalation and analyst workflows | Map a basic SOC workflow |
| 4. Log analysis and anomaly detection | Log sources, event patterns, baselines and unusual activity | Review an authorised sample dataset |
| 5. Phishing and social engineering defence | Email indicators, suspicious links, sender analysis and awareness | Assess sample messages safely |
| 6. Threat intelligence | Indicators, context, enrichment, source reliability and reporting | Prepare a structured threat summary |
| 7. Incident response assistance | Timelines, evidence handling, severity, communication and playbooks | Draft an incident report from sample evidence |
| 8. AI application security | Prompt injection, data exposure, unsafe outputs, tool permissions and validation | Identify risks in a sample LLM application design |
| 9. AI governance and privacy | Data minimisation, access control, human oversight, auditability and policy | Create a basic AI security checklist |
| 10. Defensive capstone | Analyse a controlled scenario, validate findings and present recommendations | Deliver a defensive investigation report |
Build a broader foundation with complementary security and technology courses.
Strengthen networking, operating systems and security principles.
Learn authorised security assessment in controlled lab environments.
Use scripting for data handling and security automation.
Understand cloud infrastructure, identity and security controls.
Contact O7 Services to confirm current course duration, fees and batch availability.
| Discuss with our team | What will be confirmed |
|---|---|
| Course track | Recommended level based on networking and security experience |
| Duration and schedule | Available duration, class timings and batch schedule |
| Fees and payment | Current fee, payment terms and applicable offers |
| Lab requirements | Required software, accounts and controlled lab setup |
| Practical work | Sample datasets and authorised exercises included in the batch |
The course is intended for learners interested in defensive cyber security and the responsible use of AI in security work.
Basic computer and networking knowledge is recommended. Learners without a security background should ask about the introductory pathway.
These skills complement foundational cyber security knowledge and can support defensive roles and AI application risk assessment.
| Role or activity | How the skills can help | Important foundations |
|---|---|---|
| SOC Analyst | Use AI-assisted context summaries and alert triage support | Networking, logs and security monitoring |
| Security Analyst | Organise evidence and investigate suspicious activity | Threat analysis and incident procedures |
| Threat Intelligence Analyst | Summarise and enrich information from trusted sources | Source validation and threat context |
| Application Security Learner | Understand risks in LLM-powered applications | Secure development and access control |
| IT Security Professional | Evaluate AI tools and security automation use cases | Risk management, governance and privacy |
Help IT and security teams understand where AI can support defensive operations and how to manage risks in AI-enabled applications.
Contact O7 Services to discuss a workshop aligned with your organisation's security requirements.
Explore feedback about O7 Services and speak with the training team about practical learning, course delivery and batch details.
It is the use of machine learning and AI-assisted systems to support security monitoring, threat analysis, anomaly detection, incident triage and defensive workflows.
Yes. The page describes defensive learning, safe lab exercises, authorised assessment, security monitoring and responsible AI use.
Topics include AI security foundations, SOC workflows, threat intelligence, phishing detection, log analysis, incident response, LLM application security and AI governance.
Basic computer and networking knowledge is recommended. The trainer can advise which batch is suitable for your current level.
No. AI systems can miss threats and produce false positives. Security findings must be validated by trained people and established controls.
Prompt injection is an attack pattern where untrusted input attempts to influence an AI system's instructions or behaviour. It is an important risk for LLM-powered applications.
Practical activities should be limited to controlled lab environments, sample datasets and systems where explicit permission has been granted.
Contact O7 Services for current batch duration, schedule and fees.
Discuss the current batch, practical projects and course schedule with our training team.
