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Renan Raad

Cybersecurity Analyst | Blue Team, Security Validation & Evidence Automation

I build local-first defensive cybersecurity workflows that turn technical work into verifiable artifacts: evidence manifests, SHA-256 hashes, validation records, risk notes, control checklists, playbooks, sanitized reports, and audit-ready technical documentation.

My current work focuses on Blue Team validation, AppSec support, secure remediation, patch validation, threat modeling, residual-risk review, and human-controlled AI-assisted security workflows.

AI is used as a support layer for review, diagnosis, documentation, and remediation planning. Security decisions remain scoped, human-reviewed, reversible where possible, and backed by traceable evidence.

Core Focus

  • Blue Team validation
  • Security validation
  • Evidence automation
  • AppSec support
  • Secure remediation
  • Patch validation
  • Threat modeling
  • Risk and residual-risk review
  • Local-first defensive automation
  • Audit-ready security documentation
  • Sanitized security reporting

Public Proof Of Work

Repository Demonstrates Evidence Type
FCCSecurity-Public Defensive console, public-release governance, validation records, residual-risk tracking Static app, documentation, release-gate evidence
ai-threat-model-dependency-risk-lab Threat modeling, dependency-risk review, remediation planning, human approval gate Threat model, dependency review, remediation plan, validation report
codex-safe-operation-lab-public Human-controlled AI workflow, Windows defensive triage, local-first evidence handling Safe operation docs, static panel, sanitized triage summary

Operating Method

Scope -> Review -> Remediate -> Validate -> Evidence -> Sanitize

Every security claim should be bounded by:

  • observed facts;
  • reasonable inferences;
  • hypotheses;
  • recommendations;
  • unknowns.

The goal is not to create vague automation. The goal is to produce defensive work that can be reviewed, reproduced, audited, or safely sanitized for portfolio use.

Evidence Model

Evidence Class Meaning
Observed Fact Directly seen in file, command output, log, screenshot, diff, hash, test, or artifact
Reasonable Inference Supported by observed facts, but not directly proven
Hypothesis Plausible but not yet validated
Recommendation Action proposed from evidence, risk, or missing validation
Unknown Data still required before a claim can be validated

Defensive Boundary

This profile is defensive only.

Public material is sanitized and does not include secrets, private host data, live target details, credential material, exploit chains, persistence, evasion, malware, or unauthorized third-party activity.

Any adversary-informed activity is treated as restricted, explicitly authorized, scoped, non-destructive, lab-safe, and used only to improve defenses, detections, remediation quality, and documentation.

Claim Boundary

This GitHub profile shows proof of work through public defensive labs and documentation.

It does not claim formal certification, third-party audit, legal attribution, complete absence of vulnerabilities, employment, membership, partnership, endorsement, or authorization outside the declared scope of each repository.

Pinned Loading

  1. ai-threat-model-dependency-risk-lab ai-threat-model-dependency-risk-lab Public

    Public sanitized defensive AI lab for threat modeling, Codex-assisted remediation, patch validation, and initial Codex Security evidence with human approval.

    JavaScript

  2. FCCSecurity-Public FCCSecurity-Public Public

    Public sanitized static preview for FCCSecurity

    JavaScript

  3. codex-safe-operation-lab-public codex-safe-operation-lab-public Public

    Public sanitized portfolio version of a local lab for safe OpenAI Codex CLI operation and defensive triage documentation.

    CSS