Securing GenAI Systems With Best Practice Frameworks - 2026

Scope GenAI deployments and compare public vs enterprise SaaS and built models for security. Understand pre-trained, fine-tuned, and self-trained options and the role of training data.

Explore how companies build genai applications using foundation models via APIs, ground responses with retrieval augmented generation from internal knowledge bases, and compare Rag, fine-tuning, and self-trained options.

Identify GenAI security risks such as prompt injections, data poisoning, model poisoning, and hallucinations, plus privacy concerns and traditional attacks. Prepare to study security frameworks.

Identify common pitfalls in securing generative AI, such as lack of awareness, overreliance on tools, and fragmented approaches, and advocate a best-practice, risk-based, threat-modeled security framework.

Adopt a best-practice generative AI security framework for threat mitigation, standardized controls, governance, and cost-efficient risk management across data, models, prompts, and outputs.

Explore the Mighty Atlas framework, a free, living knowledge base of adversarial threats to AI systems, guiding threat modeling, red teaming, and case studies across the AI life cycle.

Apply mitre atlas to threat model ai systems and guide red team testing for a generative ai chat app, addressing data poisoning, prompt injection, and api exploits.

Explore the OWASP top ten for large language models and a case study on implementing the framework to mitigate prompt injection, data leakage, and other LM risks.

Apply the owasp top ten to threat model a genai system in finance, addressing prompt injection, data disclosure, and model poisoning with guardrails and human oversight.

Explore the AWS generative AI security scoping matrix, mapping five deployment scopes from public services to self-trained models, and prioritize governance, risk management, privacy, controls, and resilience.

Explore the AWS generative AI security scoping matrix across governance, compliance, legal, risk, and controls, with threat modeling from OWASP and Mitre guiding data, training, and resilience.

Apply the AWS generative AI security scoping matrix to a two-use-case case study: a pre-trained chatbot on bedrock and a fine-tuned marketing model, addressing governance, legal, risk, controls, and resilience.

Examine how a cloud-based fine-tuned gen ai model uses anonymized customer data to craft personalized marketing campaigns, guided by governance, ethics, legal compliance, risk management, and strong security controls.

Explore Google's secure AI framework, Safe, a conceptual approach to secure AI systems with six core elements and a four-step implementation: understand use case, assemble teams, raise awareness, apply elements.

Implement the Google secure AI framework with Atlas for threat modeling and OS security guidance. Apply six core elements of Google Safe to securely deploy generative AI and mitigate risks.

Explore emergent generative AI security tools, such as guardrails and large language model firewalls, within a defense-in-depth framework. Learn how AI security posture management guides CISOs across multi-cloud genAI deployments.

Create a compliant, risk-aware generative AI security framework by integrating regulations, ISO 2701, risk management, and Atlas with the AWS Generative AI Security Matrix and OWASP.

Continue your learning journey in GenAI security by studying evolving best-practice security frameworks, applying one to your company or a sample project, and staying updated on industry trends.