Attended the free webinar? This programme teaches you to build the defenses that stop every attack you saw, with real systems, adversarial testing, and a structured capstone. Haven't attended yet? Start there.
Defensive AI
Security
Design and operate production-grade AI defenses. Build guardrails, validate inputs, implement behavioral monitoring, and harden AI systems against real adversarial pressure.
Eight weekends. Four build cycles.
Each module pairs theory with a shipped defensive component. Every block ends in a lab that measures your defenses against real attack payloads, not slides.
Defensive Architecture & Input Validation
- Defense-in-depth principles applied to AI system architecture
- System prompt hardening: structure, constraints, and role definitions
- Input sanitization and normalization for LLM endpoints
- Context window management and injection boundary enforcement
- Adversarial input patterns and proactive defense design
Harden a vulnerable chatbot against direct and indirect prompt injection. Measure before/after attack success rates.
Guardrails & Content Safety Engineering
- Guardrail architecture: layered safety pipeline design
- Output content filtering and toxicity classification
- Intent classification systems for abuse prevention
- Adversarial testing of safety systems: finding evasion paths
- Evasion-resistant design: why single-layer guardrails fail
Build and adversarially test a multi-layer guardrail system using real attack payloads. Iterate until the defense holds.
Monitoring, Detection & Incident Response
- Behavioral anomaly detection for AI system abuse
- Prompt injection detection strategies in production
- Telemetry logging design for LLM deployments
- Alert design and triage for AI-specific security incidents
- Incident response playbooks for model abuse and data exfiltration
Implement a detection pipeline that identifies prompt injection attempts in a simulated production environment and generates structured alerts.
Production Hardening & Capstone
- Secure deployment patterns: API gateways, rate limiting, and auth for AI
- CI/CD security integration for AI components and model updates
- Third-party model and vendor risk management
- Red vs. blue capstone: attack your own defenses under time pressure
- Measuring and reporting defensive effectiveness to engineering teams
Capstone: design, build, and adversarially validate a complete defensive AI stack. Defend against a structured red team.
Walk out with shippable defenses.
- Design production-grade guardrails that withstand real adversarial pressure
- Build input validation and output filtering pipelines for LLM deployments
- Implement behavioral monitoring and alert systems for AI-specific threats
- Harden AI systems across the full deployment and delivery stack
- Integrate AI security practices into CI/CD and development workflows
- Measure and communicate defensive effectiveness through structured red team exercises
What you need coming in.
- Python or similar scripting experience
- Familiarity with APIs and web service architectures
- Security engineering background helpful but not required
- No ML or AI background required
- Security engineers responsible for LLM-powered application security
- Platform or backend engineers owning AI system integrations
- Application security engineers reviewing AI features and APIs
- Trust and safety engineers building content moderation pipelines
- Staff engineers defining safety architecture for product teams
AI is deployed. Security is an afterthought.
Every security team is being asked to review AI systems they have never tested. LLMs are shipped into production daily, with no threat model, no red team, no security review.
Traditional security training has not kept up. VAPT courses teach web apps. AppSec programmes cover OWASP Top 10. None of them touch what actually happens when an attacker targets an LLM, a RAG pipeline, or an autonomous agent.
This programme was built to close that gap, for practitioners who want to be the person in the room who actually understands AI Security, not just knows the acronyms.
Security teams have zero hands-on experience testing AI. Attackers are already exploiting this.
Most courses teach AI security as theory. No labs. No real systems. No way to verify you can actually do it.
You break a real AI system. Then you secure it. Then you map it to a governance framework. In one structured journey.
Not a course. A transformation.
There are courses that teach AI Security. There are none that teach you to break it, secure it, and govern it end to end, in labs, with real evaluation.
Browser-based labs. Ready in seconds.
No setup. No installation. No wasted time configuring environments. Every lab runs in your browser, fully pre-configured, with a real vulnerable AI application waiting for you.
You break real systems. Not toy examples.
Pre-built vulnerable AI applications designed to mirror production deployments: an LLM chatbot, a RAG pipeline, an autonomous agent. You attack them the same way an adversary would.
Attack → Defend → Govern. One journey.
Not three separate courses. A single, structured learning arc that takes you from exploiting a vulnerability to securing it to mapping it to a compliance framework. Nothing else does this.
OSCP-style rigour. Not attendance-based.
You earn your certificate by passing a real evaluation, not by showing up. The exam tests whether you can actually execute what you learned, against an unseen target system.
Structured like SANS. Not a YouTube playlist.
Eight weekends of deliberate, sequenced learning. Each module builds on the last. No fragmented content. No jumping between topics. A real curriculum with real progression.
Built on industry frameworks.
Every concept maps to NIST AI RMF, MITRE ATLAS, ISO/IEC 42001, and OWASP LLM Top 10. You leave with vocabulary and structure that translates directly to professional work.
You do not read about attacks. You execute them.
The lab environment is the programme. Every concept taught in a session has a corresponding lab that runs the same day. Theory without practice is not security training.
Pre-built Vulnerable AI Applications
Every lab runs against a real application: a production-style LLM chatbot with RAG, an autonomous agent with tool access, or an AI API with authentication. These are not toy examples. They model how AI is actually deployed.
Browser-Based. No Setup Required.
Labs spin up in your browser in under 60 seconds. No Docker. No VMs. No stack traces at 10pm. You land on the lab and start attacking. The environment handles everything else.
Guided Exploitation Paths
Every lab includes a structured exploitation path: a sequence of steps that takes you from reconnaissance to confirmed exploit to findings documentation. You learn technique, not just outcome.
Open-Ended Challenges
Beyond the guided path, each lab contains open-ended challenges: edge cases, multi-step attack chains, and scenarios that require you to think, not just follow instructions.
On-Demand Lab Access Throughout
Every lab environment stays available for the full duration of your programme, not just during class. Revisit, retry, and explore at your own pace. AI security is learned by doing, and doing takes time.
The real test is a real system.
The capstone is the full programme in one exercise. You assess a complete AI system, end to end. Threat model it. Break it. Secure it. Govern it. This is what professional AI security work looks like.
Threat Model the System
You receive a real-world AI application: a fully deployed LLM system with RAG, agents, and API integrations. Your first task: map the attack surface, identify trust boundaries, and produce a complete threat model using MITRE ATLAS and NIST AI RMF.
Exploit It
Now you attack. Using your threat model as a guide, you attempt to execute real vulnerabilities: prompt injection, agent manipulation, data exfiltration, guardrail bypass. Every finding is documented with severity, reproduction steps, and evidence.
Secure It
You switch sides. For each confirmed vulnerability, you design and implement a defence: guardrail updates, input validation, system prompt hardening, monitoring rules. You re-run your own attacks to verify the fix holds.
Map It to Governance
Finally, you document everything: risk register, control mappings to NIST AI RMF and ISO/IEC 42001, residual risk assessment, and remediation roadmap. A deliverable you can walk into any organisation and present.
What You Submit
You earn it. You don't just receive it.
Attendance-based certificates signal nothing to an employer. Real AI Security skill is demonstrated under examination conditions, against a system you have never seen.
Participation Certificate
Awarded on completing all programme sessions and lab exercises. Confirms you completed the training, the baseline that every graduate receives.
Exam Clearance Certificate
Awarded on passing the final assessment: a timed, practical examination against an unseen AI system. This certificate signals genuine, demonstrated competence, not attendance.
How the Exam Works
The final examination is a structured, time-bound practical challenge. You receive access to an unseen AI system and a set of objectives: find vulnerabilities, demonstrate exploitation, document findings, and propose remediation. Results are evaluated on technical accuracy and quality of deliverables, not multiple-choice.
The smartest investment in your AI security career.
sudolearning is priced at 3× less than the nearest comparable training, and delivers more: live labs, real systems, and an exam that proves you can do it.
No dedicated AI Security track. No hands-on AI labs. Expensive travel and exam fees not included.
No labs. No evaluation. No instructor. No capstone. Certificates that mean nothing.
One-day exposure. No structured curriculum. No follow-up labs or evaluation.
48 hours live. Browser-based labs. Real system capstone. OSCP-style exam. A fraction of SANS pricing.
What you are actually paying for
Early cohort pricing available. 100+ professionals already registered.
Harden the systems everyone relies on.
Eight weekends of structured, lab-first defensive AI engineering. Seats are filling fast.
100+ professionals already registered. Reserve your seat early.