Building Secure and Responsible LLM Applications Training Course
LLM application security is the discipline of designing, building, and maintaining safe, trustworthy, and policy-compliant systems using large language models. This instructor-led, live training (available online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered output. Participants will learn to incorporate security controls such as input validation, human-in-the-loop oversight, and output guardrails.
By the end of this training, participants will be able to:
- Understand the core vulnerabilities of LLM-based systems.
- Apply secure design principles to LLM application architecture.
- Use tools such as Guardrails AI and LangChain for validation, filtering, and safety measures.
- Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options for Government
- To request a customized training for government agencies, please contact us to arrange.
Course Outline
Overview of LLM Architecture and Attack Surface for Government
- How Large Language Models (LLMs) are constructed, deployed, and accessed through APIs.
- Key components in LLM application stacks, such as prompts, agents, memory, and APIs.
- Identifying where and how security issues manifest in real-world usage for government.
Prompt Injection and Jailbreak Attacks for Government
- Definition of prompt injection and its potential risks.
- Scenarios involving direct and indirect prompt injection.
- Techniques used to bypass safety filters, known as jailbreaking.
- Strategies for detecting and mitigating these attacks in government applications.
Data Leakage and Privacy Risks for Government
- Accidental exposure of sensitive data through LLM responses.
- Risks associated with the leakage of Personally Identifiable Information (PII) and misuse of model memory.
- Best practices for designing privacy-conscious prompts and implementing retrieval-augmented generation (RAG).
LLM Output Filtering and Guarding for Government
- Utilizing Guardrails AI to filter and validate content outputs.
- Defining output schemas and constraints to ensure compliance with government standards.
- Monitoring and logging unsafe outputs to maintain security and accountability.
Human-in-the-Loop and Workflow Approaches for Government
- Determining the appropriate points for human oversight in LLM processes.
- Implementing approval queues, scoring thresholds, and fallback handling mechanisms.
- Calibrating trust levels and emphasizing the role of explainability in government workflows.
Secure LLM App Design Patterns for Government
- Applying principles of least privilege and sandboxing to API calls and agents.
- Implementing rate limiting, throttling, and abuse detection mechanisms.
- Ensuring robust chaining with tools like LangChain and maintaining prompt isolation.
Compliance, Logging, and Governance for Government
- Guaranteeing the auditability of LLM outputs to meet regulatory requirements.
- Maintaining traceability and control over prompts and versions used in government applications.
- Aligning with internal security policies and external regulatory needs to ensure compliance.
Summary and Next Steps for Government
Requirements
- An understanding of large language models and prompt-based interfaces for government applications
- Experience in developing LLM applications using Python
- Familiarity with API integrations and cloud-based deployments in a public sector environment
Audience
- AI developers working in the public sector
- Application and solution architects for government projects
- Technical product managers involved with LLM tools in government settings
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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