---
url: "https://xcademia.com/courses/ai-powered-attacks-and-autonomous-threat-defence"
title: "AI-Powered Attacks and Autonomous Threat Defence"
description: "Three-day practitioner training on AI attacks, adversarial ML, MLSecOps, prompt injection, and AI SOC design, aligned with MITRE ATLAS and NCSC AI guidance."
publishedAt: "2026-04-13T06:06:46.957702+00:00"
updatedAt: "2026-04-29T05:01:21.381109+00:00"
type: course
code: "CYB-0156"
level: Practitioner
duration_days: "3"
track: "Cyber Warfare & Advanced Threat Defence"
category: "Cybersecurity & Ethical Hacking"
credential_tier: tier1
price_gbp: "3695"
---

# AI-Powered Attacks and Autonomous Threat Defence

> A practitioner programme covering AI-generated cyberattacks, autonomous reconnaissance, adversarial machine learning, MLSecOps, and AI-powered defensive security operations. Develop the skills to identify AI-assisted attack patterns across the full attack lifecycle, secure AI pipelines against adversarial manipulation, and design an AI-augmented SOC for your organisation.

## Overview

Artificial intelligence has fundamentally changed the economics and speed of cyberattacks. Over 82% of phishing emails now contain AI-generated elements. Adaptive malware can modify its behaviour during an active attack using language-model-based components. Autonomous reconnaissance operates at a scale and speed impossible for human operators, discovering and exploiting vulnerabilities faster than defenders can respond. Security teams that do not understand these capabilities cannot build effective defences against them.

Over three mentor-led days, participants identify AI-generated attacks across the full attack lifecycle, apply AI-powered defensive tools to detect threats at machine speed, assess and defend AI and ML systems against adversarial attacks including poisoning, evasion, and model theft, build detection rules for AI-assisted attack patterns, secure LLM deployments against prompt injection, and evaluate AI security tooling against organisational requirements.

The programme concludes with a capstone designing a complete AI-augmented SOC playbook for a mid-size enterprise, defining tool roles, human escalation points, and adversarial ML defences. This course is aligned with MITRE ATLAS, NCSC AI security guidance, and MLSecOps industry standards.

## Prerequisites

- Active professional experience in a SOC, security architecture, or security engineering role.
- Basic understanding of machine learning concepts, preferably with some exposure to AI or ML tools.
- Familiarity with cybersecurity fundamentals including threat detection, SIEM platforms, and incident response.

## What you will learn

- Identify AI-generated attacks including adaptive malware, synthetic phishing, and autonomous reconnaissance.
- Apply AI-powered defensive tools to detect threats at machine speed across enterprise SOC environments.
- Assess AI and ML systems against adversarial ML attacks including poisoning, evasion, and model theft.
- Build detection rules and monitoring strategies specifically for AI-assisted attack patterns.
- Apply MLSecOps principles to secure AI systems deployed in production security operations contexts.
- Design an AI-augmented SOC playbook with clear human-AI interaction boundaries and escalation workflows.
- Evaluate AI security tooling against your organisation's specific threat profile and operational requirements.

## Skills you will gain

- AI attack pattern identification
- Adversarial ML defence implementation
- MLSecOps lifecycle integration
- Prompt injection mitigation
- AI-augmented SOC design
- AI security tooling evaluation
- AI pipeline security assessment
- Behavioural anomaly detection with AI
- LLM deployment security hardening

## Career progression

- SOC Analyst
- Threat Hunter
- Security Architect
- AI Security Engineer
- Detection Engineer
- CISO

## Curriculum

1. **Module 1: Getting Ready**
   - Pre-reading: MITRE ATLAS framework overview and NCSC AI security guidance
   - Introduction to the AI attack and defence taxonomy used throughout the programme
   - Accessing the lab environment, AI threat intelligence datasets, and detection platform access
   - Course objectives, AI security knowledge baseline self-assessment, and pathway alignment
2. **Module 2: AI-Generated Malware and Adaptive Attack Tooling**
   - Over 82% of phishing emails now contain AI-generated elements: what this looks like in practice
   - Experimental malware families that modify their behaviour during attacks using LLM-based components
   - AI-assisted vulnerability discovery: accelerating zero-day identification and exploitation timelines significantly
   - Autonomous code analysis tools capable of identifying severe vulnerabilities at scale in short timeframes
   - Detection strategies for AI-generated malware: why behavioural detection outperforms signature-based approaches
3. **Module 3: Autonomous Phishing and Reconnaissance at Scale**
   - AI-powered phishing campaigns that adapt tone, language, and content based on target response patterns
   - Automated target profiling: scraping personal data to craft hyper-personalised spearphishing lures at volume
   - Autonomous network and application reconnaissance: what AI-enabled discovery looks like at enterprise scale
   - Deepfake voice and video integrated into AI-automated social engineering chains targeting staff
   - Defending against AI-powered social engineering at the organisational level through policy and tooling
4. **Module 4: AI-Driven Defensive Anomaly Detection**
   - Behavioural analytics for insider threat and APT detection at machine processing speed
   - AI-powered UEBA: detecting anomalies in user and entity behaviour that rules-based systems consistently miss
   - Network traffic analysis using machine learning for C2 communication detection
   - AI-augmented SOC: what AI genuinely does well and where human judgment remains essential and irreplaceable
   - Evaluating AI security tooling before procurement: the questions that matter operationally
5. **Module 5: Adversarial Machine Learning: Attacks on AI Systems**
   - Data poisoning attacks: corrupting ML model training data to degrade security system performance
   - Evasion attacks: crafting adversarial inputs that bypass AI-powered security controls at inference time
   - Model inversion and extraction attacks: stealing proprietary model behaviour and reconstructing training data
   - Documented adversarial ML attacks against production AI-powered security systems in the wild
   - Hardening ML models against adversarial manipulation: defensive techniques and implementation guidance
6. **Module 6: Securing AI Pipelines and Training Data**
   - Supply chain security for AI systems: model provenance verification, data lineage tracking, and dependency management
   - Training data integrity: detecting and preventing data poisoning during ML model development cycles
   - Model registry security: protecting trained models from tampering, theft, and unauthorised modification
   - CI/CD pipeline security for ML model deployment: integrating security gates into automated ML workflows
   - Monitoring AI systems in production for evidence of ongoing adversarial manipulation or model drift
7. **Module 7: MLSecOps: Security for ML Model Lifecycles**
   - What MLSecOps is and why standard DevSecOps practices need significant adaptation for AI systems
   - Integrating security testing and review into the ML model development and training lifecycle
   - Automated security testing for AI models: what to test, how to fail safely, and remediation approaches
   - Logging, observability, and audit trails for AI systems deployed in security-critical operational contexts
   - Incident response for a compromised, manipulated, or poisoned AI security tool in production
8. **Module 8: Prompt Injection and LLM Deployment Security**
   - Prompt injection attacks: hijacking LLM behaviour through crafted inputs in enterprise deployments
   - Direct versus indirect prompt injection: different attack vectors and their implications for SOC automation
   - Securing LLM deployments integrated into SOC automation, alert triage, and security operations workflows
   - Output validation, content filtering, and sandboxing for AI systems with security-sensitive outputs
   - Hands-on practical: identify and mitigate prompt injection vulnerabilities in a simulated LLM deployment
9. **Module 9: AI Security Tooling Evaluation Framework**
   - Structured framework for evaluating AI-powered security tools before procurement or deployment
   - Key evaluation dimensions: training data transparency, model explainability, adversarial robustness, and drift detection
   - Benchmarking AI security tools against your specific threat profile and data environment characteristics
   - Integrating AI tools into existing SOC workflows without creating dangerous over-reliance on automation
   - Building a phased AI security tooling adoption roadmap aligned to your organisation's security maturity
10. **Module 10: AI Governance in Cyber Warfare Context**
   - EU AI Act: high-risk AI system classification and its implications for AI used in security operations
   - Governance requirements for AI systems making or influencing security-critical decisions
   - Accountability and audit requirements for AI-assisted threat detection and response decisions
   - Ethical considerations in deploying AI for defensive cyber operations against nation-state adversaries
   - Building an internal AI governance framework for security operations use cases
11. **Module 11: Capstone: AI-Augmented SOC Playbook Design**
   - Design a complete AI-augmented SOC playbook for a defined mid-size enterprise security operations team
   - Define AI tool roles clearly: automated detection responsibilities, human escalation triggers, and false positive management
   - Integrate adversarial ML defences into SOC monitoring rules and incident response procedures
   - Produce a prompt injection mitigation plan for any LLM-powered automation in the SOC workflow
   - Present the completed AI-augmented SOC design to a simulated leadership audience with instructor debrief

## Exam & certification

You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**1.  Do I need a machine learning or data science background?**

No. Basic familiarity with AI and ML concepts is helpful but not required. Adversarial ML and AI security content is taught from accessible first principles for security professionals without a data science background.

**2.  Does this course cover both using AI for defence and defending against AI attacks?**

Yes. The programme covers the complete landscape: understanding AI-powered offensive attacks, applying AI defensively in SOC operations, and securing AI systems themselves against adversarial manipulation.

**3.  How current is the AI threat content?**

This is one of the fastest-evolving areas in cybersecurity. Content is updated continuously and instructors draw on live AI threat intelligence, current attack tooling categories, and active adversarial ML research throughout the programme.

**4.  What do I leave with?**

A Certificate of Achievement, a completed AI-augmented SOC playbook from the capstone, adversarial ML exercise outputs, an AI security tooling evaluation framework, and a personal action plan.

**5.  Does this course need an exam?**

No. Assessment is through hands-on labs and the Day 3 AI SOC design capstone. Completion requires full attendance and delivery of the completed capstone playbook.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | CYB-0156 |
| Duration | 3 days |
| Level | Practitioner |
| Track | Cyber Warfare & Advanced Threat Defence |
| Category | Cybersecurity & Ethical Hacking |
| Credential tier | tier1 |
| Price (GBP) | £3695 |

---

## About this content

This Markdown course profile is the citation-grade twin of [AI-Powered Attacks and Autonomous Threat Defence](https://xcademia.com/courses/ai-powered-attacks-and-autonomous-threat-defence). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

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- Publisher: Xcademia — https://xcademia.com
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