SaaS Compliance Automation with a Python Stack
summary
Catalogs tens of thousands of SaaS apps, builds per-app security/compliance profiles from netflow, web search results and news, and reduces manual work to a single analyst; pipeline runs for 3+ years and handled a 2023 traffic spike in minutes. Uses PySpark vs Athena for big data querying, NLTK + Sklearn then a pretrained sentiment model (67M params) for news sentiment, trains a random forest on 100 hand-labeled apps for feature weights, and stores results in MongoDB via PyMongo successfully.
AI-written summary · about
chapters (6)
- 0:00Introduction to SaaS compliance
- 3:56Application catalog overview
- 9:35System architecture deep dive
- 16:25Compliance and risk scoring
- 19:18Real-world use cases
- 21:04Summary and Q&A
related talks
48mTwo Steps Forward for SaaS Adoption, One Step Back for Security
12mUsing AI/ML to scale governance, risk management, and audits
30mPySecure: Your Security One Stop Shop using Python
20mSetting the standard in cloud security
28mScaling Security Anomaly Detection in Enterprise Knowledge Base with Dask
21mHyperautomating Compliance and Risk Intelligence in Finance
29mAutomating Continuous Compliance for Scalable SaaS Delivery
01h 02mUsing Python to Power Serverless Applications