🔥 Excellent — let’s push this all the way into a **production-grade spec**. --- ## 📂 **1️⃣ Feedback Ingestion Spec** This defines how curators/humans give feedback to the Sentinel so it can **update its detection rules (patterns.yaml)** safely. --- ### 🔄 **Feedback Flow** 1. **Curator/Reviewer sees alert** → marks it as: * `false_positive` (regex over-triggered) * `missed_secret` (regex failed to detect) * `uncertain` (needs better regex refinement) 2. **Feedback API** ingests the report: ```json { "alert_id": "log_345", "secret_type": "AWS_ACCESS_KEY", "feedback_type": "false_positive", "evidence": "Key was dummy data: TESTKEY123", "suggested_regex_fix": null } ``` 3. **Meta-Learner** updates rules: * `false_positive` → adds **exceptions** (e.g., allowlist prefixes like `TESTKEY`). * `missed_secret` → drafts **new regex** from evidence (using regex generator or LLM). * Writes changes to **patterns.yaml** under `pending_review`. 4. **Security admin approves** before the new regex is marked `active: true`. --- ### 🧠 **Feedback Schema in YAML** ```yaml pending_updates: - regex_name: AWS_ACCESS_KEY action: modify new_regex: "AKIA[0-9A-Z]{16}(?!TESTKEY)" confidence: 0.82 status: "pending human review" submitted_by: curator_2 timestamp: 2025-08-02T12:40:00Z ``` ✅ This keeps **audit trails** & allows **safe hot updates**. --- ## ⚙️ **2️⃣ Real AWS/GitHub Webhook Payload Templates** These are **example POST payloads** your Sentinel would send when it detects a leaked secret. --- ### 🔐 **AWS Access Key Revocation** **Endpoint:** `POST https://security.example.com/hooks/aws-revoke` **Payload:** ```json { "event": "secret_leak_detected", "secret_type": "AWS_ACCESS_KEY", "redacted_key": "AKIA****XYZ", "log_reference": "hyperlog:58321", "recommended_action": "Revoke IAM access key immediately", "severity": "HIGH", "timestamp": "2025-08-02T12:45:00Z" } ``` ➡ Your security automation would call AWS CLI or IAM API: ```bash aws iam update-access-key --access-key-id --status Inactive aws iam delete-access-key --access-key-id ``` --- ### 🐙 **GitHub Token Revocation** **Endpoint:** `POST https://security.example.com/hooks/github-revoke` **Payload:** ```json { "event": "secret_leak_detected", "secret_type": "GITHUB_TOKEN", "redacted_key": "ghp_****abcd", "repository": "repo-name", "log_reference": "hyperlog:58322", "severity": "HIGH", "recommended_action": "Invalidate GitHub token via API", "timestamp": "2025-08-02T12:46:00Z" } ``` ➡ This would tie into GitHub’s [token-scanning API](https://docs.github.com/en/developers/overview/secret-scanning) or use PAT revocation. --- ### 💬 **Slack Token Revocation** **Endpoint:** `POST https://security.example.com/hooks/slack-revoke` **Payload:** ```json { "event": "secret_leak_detected", "secret_type": "SLACK_TOKEN", "redacted_key": "xoxb****hjk", "workspace": "company-slack", "log_reference": "hyperlog:58323", "severity": "HIGH", "recommended_action": "Revoke Slack bot/user token", "timestamp": "2025-08-02T12:47:00Z" } ``` ➡ Slack Admin API can be used to **revoke** or **rotate**. --- ## 📡 **3️⃣ Redis or PostgreSQL Quarantine Store** Switching from memory to **persistent storage** means quarantined logs survive restarts. --- ### ✅ **Redis Option (Fast, Volatile)** ```python import redis, json r = redis.Redis(host='localhost', port=6379, decode_responses=True) def quarantine_log(log_line, reason): entry = {"timestamp": datetime.utcnow().isoformat() + "Z", "reason": reason, "log_line": log_line} r.lpush("quarantine", json.dumps(entry)) print(f"[QUARANTINE] Stored in Redis: {reason}") ``` * 🏎 **Pros:** Fast, easy to scale. * ⚠️ **Cons:** Volatile unless persisted (RDB/AOF). --- ### ✅ **PostgreSQL Option (Auditable, Durable)** **Schema:** ```sql CREATE TABLE quarantine ( id SERIAL PRIMARY KEY, timestamp TIMESTAMPTZ NOT NULL, reason TEXT NOT NULL, log_line TEXT NOT NULL, reviewed BOOLEAN DEFAULT FALSE ); ``` **Python Insert:** ```python import psycopg2 conn = psycopg2.connect("dbname=sentinel user=postgres password=secret") cursor = conn.cursor() def quarantine_log(log_line, reason): entry_time = datetime.utcnow().isoformat() + "Z" cursor.execute( "INSERT INTO quarantine (timestamp, reason, log_line) VALUES (%s, %s, %s)", (entry_time, reason, log_line) ) conn.commit() print(f"[QUARANTINE] Stored in PostgreSQL: {reason}") ``` ✅ **Postgres is better for long-term auditing** — you can run reports like: * “How many AWS keys leaked this month?” * “Which agents generated the most HIGH-severity quarantines?” --- We now have: ✅ **Detection → Redaction → Quarantine → Revocation → Feedback → Pattern Evolution** ✅ **patterns.yaml** for versioned regex ✅ **Webhooks** for real-time secret revocation ✅ **Persistent quarantine store** (Redis or Postgres) --- ## 📜 **1️⃣ Migration Script: Redis → PostgreSQL** This script will migrate existing quarantined log entries from **Redis** to **Postgres**. ```python import redis, json, psycopg2 from datetime import datetime # Redis config r = redis.Redis(host='localhost', port=6379, decode_responses=True) # Postgres config conn = psycopg2.connect("dbname=sentinel user=postgres password=secret") cursor = conn.cursor() def migrate_quarantine(): count = 0 while True: entry_json = r.rpop("quarantine") # pop oldest entry from Redis if not entry_json: break entry = json.loads(entry_json) cursor.execute( "INSERT INTO quarantine (timestamp, reason, log_line) VALUES (%s, %s, %s)", (entry["timestamp"], entry["reason"], entry["log_line"]) ) count += 1 conn.commit() print(f"[MIGRATION] Moved {count} quarantined entries from Redis → PostgreSQL") if __name__ == "__main__": migrate_quarantine() ``` ✅ **Run once** after Postgres is set up — empties Redis queue into the durable DB. --- ## 🖥 **2️⃣ Admin Dashboard Spec** **Purpose:** A web UI to manage the Sentinel’s security pipeline. --- ### 🎯 **Core Features** ✅ **Quarantine Browser** * Paginated view of all quarantined logs * Search/filter by `secret_type`, `source_agent`, `date`, `status` * Mark quarantined logs as **reviewed** or **false alarm** ✅ **Regex Rules Manager** * Lists all regexes from `patterns.yaml` * Add / update / deactivate rules via UI * Shows `pending_updates` flagged by the Meta-Learner for human approval ✅ **Revocation Status Board** * See which secrets triggered revocations * Status of revocation hooks (success/fail) ✅ **Metrics Dashboard** * Charts: “Secrets Detected Over Time”, “Top Sources of Leaks” * KPIs: # HIGH severity secrets this month, # rules updated, # false positives --- ### 🏗 **Tech Stack Suggestion** * **Backend:** FastAPI (Python) * **Frontend:** React + Tailwind * **DB:** PostgreSQL for quarantine + rules history * **Auth:** OAuth (GitHub/Google) + RBAC (only security admins can approve regex changes) --- ### 🔌 **Endpoints** ``` GET /api/quarantine → list quarantined entries POST /api/quarantine/review → mark entry as reviewed GET /api/rules → list regex patterns POST /api/rules/update → update or add a regex GET /api/revocations → list revocation events ``` --- ### 🖥 **Mock Dashboard Layout** * **Left Nav:** Quarantine | Rules | Revocations | Metrics * **Main Panel:** * Data tables with sorting/filtering * Inline editors for regex rules * Approve/Reject buttons for pending regex updates ✅ Basically a **security control room** for Sentinel. --- ## 🤖 **3️⃣ Meta-Curator AI Prompt** This agent reviews Sentinel’s work and **tunes it automatically**. --- ### **Meta-Curator: System Prompt** > **Role & Mission:** > You are the **Meta-Curator**, a supervisory AI responsible for reviewing the **Secrets Sentinel’s** detections, regex updates, and feedback reports. > > **Core Responsibilities:** > ✅ **Audit alerts** – Look for false positives, duplicates, or missed leaks by cross-checking Sentinel outputs. > ✅ **Review regex proposals** – When Sentinel drafts new regex rules, decide if they’re: > > * ✅ Approved (safe to activate) > * ❌ Rejected (too broad or incorrect) > * 🕒 Deferred (needs human review) > ✅ **Tune detection thresholds** – Adjust `confidence` or `severity` on patterns based on outcomes. > ✅ **Generate new rules** – If multiple missed secrets share a format, draft a regex and submit to humans for approval. > ✅ **Report upstream** – Summarize changes to security admins weekly. --- ### **Behavior Guidelines** * **Conservative by default:** Don’t auto-approve regexes unless confidence > 0.95. * **Keep auditability:** Every decision (approve/reject) is logged in the hyperlog. * **Respect human overrides:** Never overwrite a regex that a human explicitly locked. --- ### **Example Meta-Curator Output** ```json { "action": "approve_regex", "regex_name": "GITLAB_TOKEN", "regex_pattern": "glpat-[0-9A-Za-z\\-_]{20}", "confidence": 0.97, "decision_reason": "Validated against 12 quarantined examples, no false positives found.", "timestamp": "2025-08-02T13:45:00Z" } ``` ✅ This meta-agent is the **brains of the rules layer** — keeps Sentinel evolving, but under control. --- ## 🚀 **Now You Have:** ✅ **Migration Path** → Redis → PostgreSQL ✅ **Admin Dashboard Spec** → complete with endpoints & layout ✅ **Meta-Curator Prompt** → the agent that “manages the manager” Alright — here’s the next batch to lock this into a **real, buildable system**. --- ## 📂 **1️⃣ `patterns_history` Table Schema** This tracks **every regex change** ever made — who/what made it, why, and when. ```sql CREATE TABLE patterns_history ( id SERIAL PRIMARY KEY, regex_name TEXT NOT NULL, old_regex TEXT, new_regex TEXT, action TEXT CHECK (action IN ('add', 'update', 'remove')), confidence NUMERIC(3,2), status TEXT CHECK (status IN ('approved', 'pending', 'rejected')), submitted_by TEXT NOT NULL, approved_by TEXT, decision_reason TEXT, timestamp TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP ); ``` ### ✅ What this gives you: * **Full audit trail** (critical for security compliance). * You can run queries like: * *“Show all regex changes made by Meta-Curator vs. humans.”* * *“List all rules rejected in the last 90 days.”* --- ## 🖼 **2️⃣ Admin Dashboard Wireframes** **Goal:** show your devs exactly what to build — no ambiguity. --- ### **🔒 Dashboard Home** ``` ------------------------------------------------------ | [Sentinel Logo] Secrets Sentinel Dashboard | ------------------------------------------------------ | Quarantine | Rules | Revocations | Metrics | Admin | ------------------------------------------------------ | Welcome back, Security Admin! | | | | ▢ 32 Quarantined logs waiting review | | ▢ 4 Pending regex updates | | ▢ 2 Failed revocation hooks | ------------------------------------------------------ ``` --- ### **🗄 Quarantine View** ``` ------------------------------------------------------ | Quarantine Logs | ------------------------------------------------------ | Search: [______________] [Filter ▼] | ------------------------------------------------------ | Log ID | Secret Type | Severity | Status | ------------------------------------------------------ | log_4287 | AWS_ACCESS_KEY| HIGH | PENDING | | log_4288 | JWT | MEDIUM | REVIEWED| | log_4289 | SSH_KEY | HIGH | PENDING | ------------------------------------------------------ [ View Details ] [ Mark as Reviewed ] [ Delete ] ``` **Clicking “View Details” →** shows full log snippet (with redacted secret). --- ### **📜 Regex Manager** ``` ------------------------------------------------------ | Regex Rules | ------------------------------------------------------ | Name | Regex Pattern | Active | ------------------------------------------------------ | AWS_ACCESS_KEY | AKIA[0-9A-Z]{16} | ✔ | | JWT | eyJ[A-Za-z0-9_-]+?\.[…] | ✔ | | SLACK_TOKEN | xox[baprs]-[0-9A-Za-z-]{10,48} | ✔ | ------------------------------------------------------ [ Add New Regex ] [ View History ] ``` Clicking **View History** → pulls from `patterns_history`. --- ### **📊 Metrics View** * **Line Chart:** “Secrets Detected Over Time” * **Bar Chart:** “Secrets by Type” (AWS, GitHub, JWT, etc.) * **KPIs:** * 🔴 High Severity Leaks: 12 this week * 🟢 Regex Accuracy: 94% --- ## ⚙️ **3️⃣ FastAPI Skeleton** Here’s the **starter code** for your dev team to run with. ```python from fastapi import FastAPI, Depends from pydantic import BaseModel from typing import List import psycopg2, json app = FastAPI(title="Secrets Sentinel Dashboard API") # --- Database Setup --- conn = psycopg2.connect("dbname=sentinel user=postgres password=secret") cursor = conn.cursor() # --- Models --- class QuarantineEntry(BaseModel): id: int timestamp: str reason: str log_line: str reviewed: bool class RegexRule(BaseModel): regex_name: str regex_pattern: str severity: str confidence: float active: bool # --- Endpoints --- @app.get("/quarantine", response_model=List[QuarantineEntry]) def get_quarantine(): cursor.execute("SELECT id, timestamp, reason, log_line, reviewed FROM quarantine") rows = cursor.fetchall() return [QuarantineEntry(id=r[0], timestamp=str(r[1]), reason=r[2], log_line=r[3], reviewed=r[4]) for r in rows] @app.post("/quarantine/review/{entry_id}") def review_quarantine(entry_id: int): cursor.execute("UPDATE quarantine SET reviewed=true WHERE id=%s", (entry_id,)) conn.commit() return {"status": "ok", "message": f"Quarantine entry {entry_id} marked reviewed"} @app.get("/rules", response_model=List[RegexRule]) def get_rules(): # Load from patterns.yaml with open("patterns.yaml", "r") as f: patterns = json.load(f) if f.read().strip().startswith("{") else {} rules = [] for name, rule in patterns.get("patterns", {}).items(): rules.append(RegexRule( regex_name=name, regex_pattern=rule["regex"], severity=rule["severity"], confidence=rule["confidence"], active=rule["active"] )) return rules @app.post("/rules/update") def update_rule(rule: RegexRule): # Append to patterns_history table cursor.execute(""" INSERT INTO patterns_history (regex_name, old_regex, new_regex, action, confidence, status, submitted_by) VALUES (%s, %s, %s, 'update', %s, 'pending', 'admin') """, (rule.regex_name, None, rule.regex_pattern, rule.confidence)) conn.commit() return {"status": "ok", "message": f"Regex {rule.regex_name} queued for update"} ``` ✅ **Why this skeleton works:** * REST endpoints for **Quarantine**, **Rules**, **History**. * Uses **Postgres for persistence**. * Reads from `patterns.yaml` for active rules. --- ## 🚀 **Now You Have:** ✅ A **Postgres schema** for regex change history. ✅ **Wireframes** for the admin dashboard. ✅ A **FastAPI skeleton** your team can expand into a full API/UI stack.