Tracks/Track 4 — AI & Enterprise Architect/4-3
Track 4 — AI & Enterprise Architect

4-3: AI Security & Zero-Trust Execution

Securing the Generative Attack Surface from injection and data leakage.

3 Lessons~45 min

🎯 What You'll Learn

  • Implement Data Loss Prevention (DLP)
  • Harden prompt injection defenses
  • Execute sandboxed code safely
  • Perform LLM ethical audits
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Syllabus Introduction2 MIN READ

AI Security & Zero-Trust Execution Playbook

Track 4-3: AI & Enterprise Architect

Securing the Generative Attack Surface from injection and data leakage.

Key Takeaways for Executive Action

  • Implement Data Loss Prevention (DLP) at the Perimeter: Mandate robust outbound DLP solutions, leveraging advanced Named Entity Recognition (NER) models for PII anonymization before any data transits to external LLM services.

  • Harden Prompt Injection Defenses: Architect system prompts with strict delimiters and adversarial training. Treat LLMs as critical data conduits susceptible to conversational SQL injection.

  • Execute Sandboxed Code Safely: Enforce Zero-Trust execution environments for all AI agents leveraging external tools. Containerization and strict scope restrictions are non-negotiable.

  • Perform Continuous LLM Ethical Audits: Establish a rigorous audit framework to identify and mitigate biases, ensure fairness, and prevent misuse or unintended consequences.

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Inference Architecture
01import { orchestrator } from '@exogram/core';
02
03const router = new AgentRouter({);
04strategy: 'COST_EFFICIENT_SLM',
05fallback: 'FRONTIER_MODEL'
06});
07
08await router.guardrail(payload);
+ 340%

Module Syllabus

Lesson 1: Part 1: Lesson 1: Prompt Injection & The Attack Surface

Interactive Module Section.

15 MIN

Lesson 2: Part 2: Lesson 2: Outbound Data Loss Prevention (DLP)

Interactive Module Section.

20 MIN

Lesson 3: Part 3: Lesson 3: Agentic Sandboxing

Interactive Module Section.

25 MIN
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