How To Read Agentic AI Source Code Step# 1 Data Flow In Plan English

Описание: https://www.skool.com/agentic-ai-3504
Connect With Me On Linkedin:   / trevinoparker  
Connect With Me On Github: https://github.com/TrevinoParker7
PDF: https://codejourney61.gumroad.com/l/o...

This dual-purpose application delivers a high-fidelity cybersecurity training and security assurance platform. It simulates rigorous, high-stakes oral certification interviews using a React frontend with AI. It demonstrates Agentic AI Security and GRC Engineering SOC 2 compliance through an automated backend that continuously tests controls, runs Red-Team simulations against OWASP LLM risks, manages deception honeypots, and generates verifiable audit evidence proving defenses are active and effective.

I am a Agentic AI Security Engineer & GRC Engineer & Vulnerability Management, with deep expertise in AI agent runtime security, adversarial ML defense, and secure system design. I specialize in protecting multi-agent and agentic AI systems against model poisoning, prompt injection, data exfiltration, and adversarial manipulation, and applying OWASP Top 10 risks and mitigations for LLMs and Generative AI applications. Experienced in IAM, SCP, RBAC/ABAC, DLP, OPA/Rego policy-as-code, secrets management, and key management, I enforce security guardrails to maintain regulatory compliance across AI/ML pipelines. VPC isolation with PrivateLink and VPC Endpoints, Private Endpoint Services, IAM roles with least privilege and permission boundaries, OIDC federation and IAM Roles Anywhere, KMS CMKs with key rotation and grants, AWS Secrets Manager with rotation and resource policies, CodeBuild/CodePipeline with ECR image scanning and signing, CloudTrail with CloudWatch Logs and Metrics, GuardDuty, Security Hub, AWS Config, EventBridge alerts, centralized logging to S3 with object lock, and SageMaker/EKS private subnets with CMK-encrypted storage.

Proven track record in embedding security automation into AI workflows, strengthening SOC operations, vulnerability detection, incident response, and compliance management. Skilled in applying AI safety frameworks (NIST AI RMF), cybersecurity standards including NIST, ISO 27001, SOC 2, SOX, HIPAA, PCI-DSS, FedRAMP, and CMMC, and red-teaming methodologies to ensure trustworthy, attack-resilient AI infrastructures.

As a GRC Engineer, I leverage automation platforms like AWS, Azure, GCP, Drata and Vanta to streamline governance, risk management, and compliance (GRC) processes, conduct risk-based assessments, threat modeling, and control gap analyses, and generate audit-ready dashboards and executive reports.

In Vulnerability Management, I orchestrate enterprise-wide programs using Tenable, Qualys, PowerShell, Python, and Bash, achieving significant reduction of critical, high, and medium vulnerabilities. I automate remediation workflows, compliance checks, and risk prioritization across Windows, Linux, and cloud environments, strengthening cybersecurity posture and operational efficiency.

By combining AI/ML security, GRC expertise, vulnerability management, and compliance with global frameworks, I ensure organizations maintain robust defenses, continuous regulatory compliance, and proactive threat mitigation for AI, GenAI, and enterprise applications.

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