Building Secure and Responsible LLM Applications Training Course
Security for Large Language Model applications involves the practice of designing, developing, and sustaining safe, reliable, and policy-compliant systems that leverage large language models.
This instructor-led, live training (available online or onsite) targets intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks tied to LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs. Participants will learn to implement security controls including input validation, human-in-the-loop oversight, and output guardrails.
Upon completion of this training, participants will be able to:
- Grasp the fundamental vulnerabilities inherent in LLM-based systems.
- Apply secure design principles to the architecture of LLM applications.
- Utilize tools like Guardrails AI and LangChain for validation, filtering, and ensuring safety.
- Integrate practices such as sandboxing, red teaming, and human-in-the-loop reviews into production-grade pipelines.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live-lab environment.
Customization Options
- To arrange customized training for this course, please get in touch with us.
Course Outline
Overview of LLM Architecture and Attack Surface
- Understand how LLMs are built, deployed, and accessed via APIs
- Identify key components in LLM app stacks (e.g., prompts, agents, memory, APIs)
- Analyze where and how security issues emerge in real-world usage
Prompt Injection and Jailbreak Attacks
- Define prompt injection and explain its dangers
- Explore direct and indirect prompt injection scenarios
- Examine jailbreaking techniques used to bypass safety filters
- Discuss detection and mitigation strategies
Data Leakage and Privacy Risks
- Address accidental data exposure through model responses
- Investigate PII leaks and misuse of model memory
- Learn to design privacy-conscious prompts and retrieval-augmented generation (RAG)
LLM Output Filtering and Guarding
- Utilize Guardrails AI for content filtering and validation
- Define output schemas and constraints
- Monitor and log unsafe outputs
Human-in-the-Loop and Workflow Approaches
- Determine where and when to introduce human oversight
- Manage approval queues, scoring thresholds, and fallback handling
- Understand trust calibration and the role of explainability
Secure LLM App Design Patterns
- Implement least privilege and sandboxing for API calls and agents
- Apply rate limiting, throttling, and abuse detection
- Develop robust chaining with LangChain and ensure prompt isolation
Compliance, Logging, and Governance
- Ensure auditability of LLM outputs
- Maintain traceability and prompt/version control
- Align operations with internal security policies and regulatory requirements
Summary and Next Steps
Requirements
- Familiarity with large language models and prompt-based interfaces
- Experience in developing LLM applications using Python
- Knowledge of API integrations and cloud-based deployments
Target Audience
- AI developers
- Application and solution architects
- Technical product managers utilizing LLM tools
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Building Secure and Responsible LLM Applications Training Course - Enquiry
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