AI Security

المعلم
م. عمر عثمان
السعر
3,000 EGP

1. Advanced Threat Vectors & Vulnerabilities

  • Prompt Injection: Direct instruction overriding vs. Indirect injection via untrusted external content (emails, tickets, documents).
  • Data Exfiltration: Exfiltration techniques via markdown image rendering, unmonitored egress endpoints, and webhooks (e.g., EchoLeak / CVE-2025-32711).
  • Model Extraction & Theft: Cloning model behavior and proprietary logic via systematic API probing and query outputs.
  • Privacy Failures: Membership Inference Attacks (MIA), training data memorization, unscrubbed PII ingestion, and sensitive data leakage.
  • Autonomous & Machine-Speed Threats: Self-healing attack chains, automated privilege escalation, lateral movement, and multi-agent reconnaissance (e.g., Jade Puffer, Anthropic Chinese Threat Actor case studies).
  • Dual-Use Risks: Leveraging AI for automated Zero-Day discovery and payload generation in legacy codebases (e.g., Aisle OpenSSL discovery).

2. Supply Chain & Dependency Security

  • AI Bill of Materials (AIBOM): Comprehensive inventory tracking base models, fine-tuning weights, training datasets, active prompts, and third-party tools.
  • Three Supply Chain Layers: Layer 1 (AI Building Blocks), Layer 2 (Technical Stack / Vector DBs), Layer 3 (Operational APIs & Keys).
  • Vendor & Ecosystem Risks: Silent model updates, configuration drift, and third-party logging creep.
  • Supply Chain Controls: Auditing dependencies, restricting source downloads to signed registries, and implementing multi-provider fallback architectures.

3. Production Operations & Runtime Security

  • Production Security Reality: Continuous security lifecycle, drift management, and managing runtime model execution risks.
  • Three Pillars of Production Defense: Access Control, Hard Runtime Constraints, and Telemetry/Logging.
  • Runtime Guardrails: Input interception/sanitization, output filters (PII redaction), command blocking, and confidence thresholds.
  • Network & Execution Boundaries: Hard network segmentation, digital geofence boundaries, and strict egress traffic monitoring/proxying.
  • Incident Response & Containment: The 15-Minute Rule, emergency kill-switches, replay logs, automated circuit breakers, and machine-speed isolation.

4. Identity, Access & Governance

  • Agentic Mental Model: Managing AI agents as "non-human employees" with delegated authority rather than static software assets.
  • Cryptographic Identity & Attribution: Unique machine-to-machine identities, non-repudiation logging, and avoiding shared service or human accounts.
  • Agent HR Lifecycle: Onboarding frameworks, mission-driven job descriptions, performance reviews for behavioral drift, and instant offboarding workflows.
  • Continuous Authorization & Secrets Management: Just-In-Time (JIT) short-lived token requests vs. long-lived pre-loaded secrets, and dynamic task-based re-authorization.
  • Privileged Actions & Oversight: Distinguishing Read vs. Privileged (state-changing) actions, mandatory human approval gates (Human-in-the-Loop), and token/resource budgeting.
  • Multi-Agent & Ecosystem Governance: Managing semantic drift in multi-agent handoffs, federated identity models, and blast radius minimization.

5. Frameworks, Compliance & DevSecOps Integration

  • Industry Frameworks: OWASP Top 10 for LLMs.
  • DevSecOps Integration: Shifting AI security left, integrating AIBOMs into CI/CD pipelines, automated static/dynamic code audits, and policy enforcement via technical controls rather than system prompts.