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 (online or onsite) is aimed at intermediate to advanced AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating 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 for government use.
- Apply secure design principles to LLM application architecture for government environments.
- Use tools such as Guardrails AI and LangChain for validation, filtering, and safety in government applications.
- Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines for government systems.
Format of the Course
- Interactive lecture and discussion tailored for government audiences.
- Lots of exercises and practice relevant to government workflows.
- Hands-on implementation in a live-lab environment designed for government scenarios.
Course Customization Options
- To request a customized training for this course, specifically tailored for government needs, 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, including prompts, agents, memory, and APIs
- Identification of where and how security issues emerge in practical use cases
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
Data Leakage and Privacy Risks for Government
- Risks of accidental data exposure through model responses
- Concerns about 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
- Establishing output schemas and constraints
- Monitoring and logging outputs that may be unsafe or non-compliant
Human-in-the-Loop and Workflow Approaches for Government
- Determining the appropriate points to introduce human oversight in LLM workflows
- Implementing approval queues, setting scoring thresholds, and managing fallback scenarios
- Calibrating trust levels and emphasizing the importance of explainability
Secure LLM App Design Patterns for Government
- Applying principles of least privilege and sandboxing to API calls and agents
- Incorporating rate limiting, throttling, and abuse detection mechanisms
- Using robust chaining techniques with LangChain and ensuring prompt isolation
Compliance, Logging, and Governance for Government
- Ensuring the auditability of LLM outputs to meet regulatory requirements
- Maintaining traceability and control over prompts and model versions
- Aligning with internal security policies and external compliance standards
Summary and Next Steps for Government
Requirements
- An understanding of large language models and prompt-based interfaces for government use.
- Experience building LLM applications using Python for government projects.
- Familiarity with API integrations and cloud-based deployments for government systems.
Audience
- AI developers for government agencies
- Application and solution architects for government initiatives
- Technical product managers working with LLM tools in the public sector
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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