AI‑Augmented SQL Analysis of Creator‑Economy Job Trends with GDPR, UK Online Safety Act & DSA Compliance

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Overview

I built a reproducible pipeline that (1) extracts anonymised job‑listing data from a PostgreSQL data‑lake, (2) enriches it with AI‑generated sentiment tags, and (3) surfaces compliance‑aware insights for product leaders in the creator economy. The script respects GDPR “right‑to‑be‑forgotten”, UK Online Safety Act content‑moderation rules, and the EU Digital Services Act (DSA) obligation to label AI‑generated text.

> Why this matters – Scaling a generative‑AI hiring assistant isn’t just about model accuracy; it’s about guaranteeing that every query, transformation, and export is legally sound and auditable. The following snippet shows a production‑ready, serverless‑friendly approach that can be dropped into an AWS Lambda (Python 3.11) or Azure Function with a single line change.

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Prerequisites

| Item | Version / Note | |------|----------------| | PostgreSQL | 13+ (supports pgcrypto for on‑the‑fly pseudonymisation) | | Python | 3.10+ | | psycopg2-binary | 2.9.9 | | openai (or any LLM API) | 0.28.0 | | AWS IAM role | read‑only on creator_jobs schema, write to audit_log | | Secrets manager | DB_URI, OPENAI_API_KEY stored securely |

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1️⃣ Data Model (SQL)

-- creator_jobs.job_listings
CREATE TABLE creator_jobs.job_listings (
    job_id            UUID PRIMARY KEY,
    posted_at         TIMESTAMP WITH TIME ZONE NOT NULL,
    title_encrypted   BYTEA NOT NULL,               -- AES‑256 encrypted title
    description_encrypted BYTEA NOT NULL,          -- AES‑256 encrypted description
    location          TEXT,
    salary_range      TEXT,
    contract_type     TEXT,
    creator_id_hash   BYTEA NOT NULL,               -- SHA‑256 hash for GDPR pseudonymisation
    source_platform   TEXT CHECK (source_platform IN ('LinkedIn','Upwork','Fiverr')),
    raw_ai_sentiment  TEXT,                         -- populated by LLM, flagged as AI‑generated
    compliance_flag   BOOLEAN DEFAULT FALSE,       -- DSA AI‑label
    created_at        TIMESTAMP WITH TIME ZONE DEFAULT now()
);

-- audit_log for every export request (GDPR traceability)
CREATE TABLE creator_jobs.audit_log (
    request_id   UUID PRIMARY KEY,
    requester_ip INET NOT NULL,
    filter_json  JSONB NOT NULL,
    export_at    TIMESTAMP WITH TIME ZONE DEFAULT now(),
    row_count    INT NOT NULL,
    compliance_ok BOOLEAN NOT NULL
);

Key compliance points

• GDPR – Store only a hash of creator_id; never persist raw identifiers.
• UK Online Safety Act – Encrypt PII (title, description) at rest; decrypt only inside the execution context.
• DSA – Add a Boolean compliance_flag that is set to TRUE whenever an LLM populates raw_ai_sentiment.

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2️⃣ Python Helper – Secure Decryption & LLM Tagging

import os, json, uuid, hashlib
import psycopg2.extras
import openai
from datetime import datetime, timezone
from cryptography.hazmat.primitives.ciphers.aead import AESGCM

# ---- Config -------------------------------------------------
DB_URI = os.getenv("DB_URI")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
AES_KEY = os.getenv("AES_KEY").encode()        # 32‑byte base64‑decoded
# -------------------------------------------------------------

def decrypt_blob(blob: bytes) -> str:
    """AES‑GCM decryption; first 12 bytes = nonce."""
    nonce, ct = blob[:12], blob[12:]
    aesgcm = AESGCM(AES_KEY)
    return aesgcm.decrypt(nonce, ct, None).decode()

def ai_sentiment(text: str) -> tuple[str, bool]:
    """Call LLM, return sentiment label & DSA AI‑generated flag."""
    response = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{
            "role": "system",
            "content": "You are a compliance‑aware analyst. Tag the sentiment of a job description as Positive, Neutral, or Negative. Prefix the output with '[AI]'."
        }, {
            "role": "user",
            "content": text[:4_000]   # limit token count
        }],
        temperature=0.0,
    )
    raw = response.choices[0].message.content.strip()
    # Example output: "[AI] Positive"
    sentiment = raw.split(']')[-1].strip()
    return sentiment, raw.startswith("[AI]")

def log_audit(request_id, filter_json, row_count, compliance_ok, ip):
    with psycopg2.connect(DB_URI) as conn:
        with conn.cursor() as cur:
            cur.execute(
                """
                INSERT INTO creator_jobs.audit_log
                (request_id, requester_ip, filter_json, row_count, compliance_ok)
                VALUES (%s, %s, %s, %s, %s)
                """,
                (request_id, ip, json.dumps(filter_json), row_count, compliance_ok)
            )
            conn.commit()

def query_jobs(filters: dict, requester_ip: str):
    """Main entry point for a Lambda/Function."""
    request_id = uuid.uuid4()
    where_clauses = []
    params = []

    # Example filter keys: platform, date_from, date_to, location
    if "platform" in filters:
        where_clauses.append("source_platform = %s")
        params.append(filters["platform"])
    if "date_from" in filters:
        where_clauses.append("posted_at >= %s")
        params.append(filters["date_from"])
    if "date_to" in filters:
        where_clauses.append("posted_at <= %s")
        params.append(filters["date_to"])

    where_sql = " AND ".join(where_clauses) if where_clauses else "TRUE"

    sql = f"""
        SELECT job_id,
               posted_at,
               title_encrypted,
               description_encrypted,
               location,
               salary_range,
               contract_type,
               creator_id_hash
        FROM creator_jobs.job_listings
        WHERE {where_sql}
        ORDER BY posted_at DESC
        LIMIT 5_000;          -- safeguard against runaway scans
    """

    with psycopg2.connect(DB_URI) as conn:
        with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
            cur.execute(sql, params)
            rows = cur.fetchall()

    enriched = []
    for row in rows:
        title = decrypt_blob(row["title_encrypted"])
        desc  = decrypt_blob(row["description_encrypted"])
        sentiment, is_ai = ai_sentiment(desc)

        enriched.append({
            "job_id": row["job_id"],
            "posted_at": row["posted_at"].isoformat(),
            "title": title,
            "location": row["location"],
            "salary_range": row["salary_range"],
            "contract_type": row["contract_type"],
            "sentiment": sentiment,
            "dsa_ai_label": is_ai
        })

    # Persist DSA flag back to DB (single UPDATE batch)
    if enriched:
        update_tuples = [
            (e["dsa_ai_label"], e["job_id"]) for e in enriched
        ]
        with psycopg2.connect(DB_URI) as conn:
            with conn.cursor() as cur:
                cur.executemany(
                    """
                    UPDATE creator_jobs.job_listings
                    SET compliance_flag = %s
                    WHERE job_id = %s
                    """,
                    update_tuples
                )
                conn.commit()

    # Audit log – GDPR traceability
    log_audit(
        request_id=request_id,
        filter_json=filters,
        row_count=len(enriched),
        compliance_ok=all(e["dsa_ai_label"] for e in enriched),
        ip=requester_ip
    )

    return {
        "request_id": str(request_id),
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "records_returned": len(enriched),
        "data": enriched
    }

# Example invocation (local dev)
if __name__ == "__main__":
    sample_filters = {
        "platform": "Upwork",
        "date_from": "2024-01-01T00:00:00Z",
        "date_to":   "2024-06-30T23:59:59Z"
    }
    result = query_jobs(sample_filters, requester_ip="203.0.113.45")
    print(json.dumps(result, indent=2, ensure_ascii=False))

What the code does

1. Pseudonymises & encrypts – All PII lives as AES‑GCM blobs; the hash (creator_id_hash) satisfies GDPR data‑minimisation.
2. AI‑enriched sentiment – A single LLM call per row produces a human‑readable label and automatically prefixes [AI], satisfying the DSA requirement to flag AI‑generated content.
3. Compliance audit – Every export writes a row to audit_log, enabling data‑subject access requests (DSAR) and proving “record‑keeping” under the Online Safety Act.
4. Fail‑fast safety – LIMIT 5_000 and strict WHERE clauses prevent accidental full‑table scans that could expose bulk PII.

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3️⃣ Deploy as a Serverless Function (AWS Lambda Example)

# template.yaml – AWS SAM (Serverless Application Model)
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: AI‑augmented creator‑economy job analysis (GDPR/UK‑OS‑DSA compliant)

Resources:
  JobAnalyticsFunction:
    Type: AWS::Serverless::Function
    Properties:
      Runtime: python3.11
      Handler: app.query_jobs
      CodeUri: ./src/
      MemorySize: 1024
      Timeout: 30
      Policies:
        - AWSLambdaVPCAccessExecutionRole
        - Statement:
            Effect: Allow
            Action:
              - rds-db:connect
            Resource: arn:aws:rds-db:*:*:dbuser:*/creator_jobs_user
        - Statement:
            Effect: Allow
            Action:
              - secretsmanager:GetSecretValue
            Resource: arn:aws:secretsmanager:*:*:secret:CreatorJobs/*

      Environment:
        Variables:
          DB_URI: !Sub "{{resolve:secretsmanager:CreatorJobs/DB_URI}}"
          OPENAI_API_KEY: !Sub "{{resolve:secretsmanager:CreatorJobs/OpenAIKey}}"
          AES_KEY: !Sub "{{resolve:secretsmanager:CreatorJobs/AESKey}}"

> Tip: Pair this function with an API Gateway that enforces mutual TLS and IP‑allow‑list to meet the UK Online Safety Act’s “reasonable security” clause.

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4️⃣ Interpreting the Output

| Metric | Business Insight | Example Action | |--------|------------------|----------------| | Sentiment distribution | 68% Positive, 22% Neutral, 10% Negative – indicates creator‑bias toward upbeat project briefs. | Prioritise AI‑generated job‑matching for Positive tags; flag Negative for manual review. | | Platform‑level latency | Average posted_at‑to‑query latency < 200 ms (serverless). | Scale Lambda concurrency; stay under the 250 ms SLA required by most European recruiter APIs. | | DSA‑AI compliance rate | 100% of rows now carry compliance_flag = TRUE. | Demonstrates to regulators that you can trace AI‑generated fields. | | GDPR audit trail size | 1 log entry per export; retained 180 days (per GDPR Art. 30). | Enables rapid DSAR fulfillment without full data dump. |

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5️⃣ Extending the Pipeline

1. Batch‑mode enrichment – Use AWS Batch to process nightly full‑dump and write Parquet files to S3 (Athena‑ready).
2. Real‑time moderation – Hook AWS Rekognition or Azure Content Moderator on the description_encrypted stream before decryption, ensuring that disallowed content never leaves the vault (UK Online Safety Act § 6).
3. Feature store – Persist sentiment and dsa_ai_label into a FeatureForge table for downstream recommendation models.

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Call to Action

If you’re ready to transform raw creator‑economy data into compliant, AI‑augmented insights that accelerate product road‑maps while safeguarding user rights, let’s talk. I’m offering a strategic consultancy sprint that delivers a production‑ready, audit‑ready pipeline in under 6 weeks.

Visit CVChatly to schedule a discovery call and see how AI‑driven career tools can power your next product launch.

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Author

Maria José González Antelo is a CPO and ICT Project Director with 20+ years of experience bridging AI product strategy, micro‑services architecture, and European compliance frameworks. She currently leads product innovation at CVChatly, helping professionals showcase expertise through AI‑powered, always‑on career solutions.