AI-Powered Cybersecurity Training

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AI-Powered Cybersecurity Training — Course brochure | Assure Success

About the course

Why take AI-Powered Cybersecurity Training?

Attackers are using AI to write convincing phishing, clone voices and speed up reconnaissance, while defenders face more alerts than any team can read. This course shows how AI helps on the defensive side and how to secure AI systems your organisation is starting to deploy.

You begin with the machine learning concepts a security practitioner actually needs: supervised and unsupervised learning, feature engineering from logs, model evaluation, and why false positives matter. You then apply them to detection tasks such as user and entity behaviour analytics, phishing and URL classification, malware triage and network anomaly detection. A major block covers large language models in security operations: using them to summarise alerts, enrich indicators, draft incident reports and assist hunts, while keeping humans in control and protecting sensitive data.

The final part flips the question and looks at attacks on AI itself, including prompt injection, jailbreaks, training-data poisoning, model theft and insecure agent tools, together with governance frameworks that organisations are adopting. Labs use accessible tools and notebooks, and a short Python familiarity is helpful but not essential.

Who should attend

● SOC analysts and threat hunters
● Security engineers and architects exploring AI-assisted tooling
● IT professionals and developers adopting AI in their products
● Risk, compliance and governance staff responsible for AI policies
● Students and early-career professionals who want future-ready security skills

Prerequisites

● Basic understanding of cybersecurity concepts such as threats, logs and incident response
● Elementary Python or scripting knowledge is helpful but not mandatory
● No prior machine learning experience required

What this certification does for you

Skills organisations are hiring for

AI-assisted operations and AI governance are growing priorities for security teams.

Practical, tool-agnostic

Concepts apply across SIEM, EDR and cloud platforms.

Defence and risk views together

You learn to use AI and to protect against its misuse.

Boosts existing credentials

Complements Security+, CySA+, CISSP and cloud security knowledge.

Curriculum

8 modules, step by step

MODULE 1

AI and machine learning primer for security

Learn the concepts needed to work with security AI.

› Supervised, unsupervised and reinforcement learning
› Features from logs, network and endpoint data
› Training, testing, precision, recall and false positives
› Limits and bias of models

MODULE 2

The AI-changed threat landscape

Understand how attackers use AI.

› AI-generated phishing and deepfake fraud
› Automated reconnaissance and malware variation
› Voice cloning and business email compromise
› Defensive awareness and verification practices

MODULE 3

Anomaly detection and behavioural analytics

Apply ML to spot unusual activity.

› UEBA concepts and baselines
› Network and authentication anomaly detection
› Clustering and outlier detection labs
› Tuning thresholds and handling alert fatigue

MODULE 4

ML for phishing, malware and fraud

Classify malicious content at scale.

› Text and URL features for phishing detection
› Static and behavioural malware features
› Training a simple classifier in a notebook
› Evaluation and deployment considerations

MODULE 5

Generative AI in security operations

Use LLMs responsibly in the SOC.

› Prompting for triage, enrichment and summarisation
› Threat hunting query generation and review
› Report and runbook drafting
› Data protection and human-in-the-loop controls

MODULE 6

Security orchestration and automation

Automate response safely.

› SOAR concepts and playbook design
› Integrating SIEM, EDR, ticketing and threat intelligence
› Approval gates, rollback and audit
› Metrics: mean time to detect and respond

MODULE 7

Securing AI and LLM applications

Protect AI systems from attack.

› OWASP Top 10 for LLM applications
› Prompt injection and jailbreaks
› Data poisoning, model theft and inversion
› Securing RAG pipelines, plugins and agents

MODULE 8

AI governance, risk and capstone

Govern AI use and apply the learning.

› NIST AI Risk Management Framework
› ISO/IEC 42001 and the EU AI Act overview
› Privacy and acceptable use policies
› Capstone: design an AI-assisted detection and response workflow

What you get

Why take AI-Powered Cybersecurity Training?

Who should attend

Prerequisites

What this certification does for you

8 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success