Building Secure and Responsible LLM Applications Training Course
Large Language Model (LLM) application security constitutes the systematic approach to developing, operating, and sustaining secure, reliable, and compliant systems that leverage generative AI technologies. This instructor-led training program, available in online or onsite formats, is designed for intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks inherent in LLM-powered applications. Key focus areas include addressing vulnerabilities such as prompt injection, data exfiltration, and unregulated outputs, while implementing robust security controls like input validation, human-in-the-loop oversight mechanisms, and output guardrails.
Upon completion of this curriculum, participants will be equipped to:
* Analyze the fundamental vulnerabilities associated with LLM-based infrastructure.
* Implement secure design principles within LLM application architecture.
* Utilize specialized tools, including Guardrails AI and LangChain, to enhance validation, filtering, and overall safety.
* Integrate operational techniques such as sandboxing, red teaming exercises, and human-in-the-loop review processes into production-grade pipelines.
**Course Delivery Format**
* Interactive instruction and facilitated discussion.
* Extensive practical exercises and skill-building activities.
* Hands-on technical implementation within a live laboratory environment.
**Customization for government**
* For government entities requiring tailored training solutions, please contact us to arrange specific program adjustments.This course is available as onsite live training in US Government or online live training.
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Course Outline
Overview of Large Language Model Architecture and Threat Landscape
- Methods for constructing, deploying, and interfacing with Large Language Models (LLMs) via application programming interfaces
- Essential components within LLM application architectures, including prompt engineering, autonomous agents, memory management, and data connectivity
- Identification of vulnerability points and security incidents in operational deployments
Prompt Injection and Adversarial Bypass Techniques
- Definition and operational impact of prompt injection vulnerabilities
- Analysis of direct and indirect injection vectors
- Methods used to circumvent safety filters and content policies
- Procedures for detection and implementation of countermeasures
Data Confidentiality and Privacy Protection
- Mitigation of unintended data exposure through model responses
- Prevention of personally identifiable information (PII) breaches and improper model memory usage
- Implementation of privacy-preserving prompt design and retrieval-augmented generation (RAG) frameworks
Output Control and Content Safeguarding
- Utilization of Guardrails AI for content validation and filtering protocols
- Establishment of strict output schemas and operational constraints
- Monitoring systems and logging mechanisms for unsafe model outputs
Human Oversight and Operational Workflows
- Criteria and points for integrating human review into automated processes
- Implementation of approval workflows, risk scoring thresholds, and contingency procedures
- Calibration of trust levels and the importance of system explainability
Secure Design Patterns for LLM Applications
- Application of least privilege principles and sandboxing for API integrations and autonomous agents
- Deployment of rate limiting, throttling measures, and abuse detection mechanisms
- Secure orchestration using frameworks such as LangChain with strict prompt isolation
Compliance, Auditability, and Governance
- Ensuring full auditability of LLM outputs and decision-making processes
- Maintenance of traceability through rigorous prompt and version control
- Alignment with internal security mandates and regulatory requirements for government
Summary and Strategic Next Steps
Requirements
* Demonstrated proficiency in the operational principles of large language models and the utilization of prompt engineering interfaces.
* Prior experience in developing large language model solutions utilizing the Python programming language.
* Working knowledge of application programming interface (API) integration and cloud infrastructure deployment.
Target Audience
- Artificial intelligence engineers and specialists
- Architects of application and solution systems
- Technical product managers engaged with large language model technologies
14 Hours
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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