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The Case For Secure Data Science
- 1. Secure Data Science Why do we need it and how do we get there
- 2. Benefits of Data Science • Cheap business analytics on demand • Decision consequences predicted in advance • Produce personalized products cheaply and efectively
- 4. Legal Compliance and Privacy • Regulations on what data, where, and from which people • Protecting sensitive information from prying eye inside and out • Guaranteeing non-discrimination in machine learning • Anti-trust and anti-competition regulations
- 5. Data Breaches and Analytics Integrity • Are we breached? By whom? What did they access? • Are our analytics correct? Are they tampered with? • Did our data transmit correctly? • Did input streams ingest correctly? • Is there malicious intent from any of our data suppliers?
- 6. Case In Point • Financial Industry • Our entire company was looted by a hacker inducing devastating trades • Health Care Industry • Massive lawsuit over mental health records made accessible by rogue analysis • Credit Reporting Bureaus • Class action lawsuit for malicious bogus content inserted by a rogue provider • Intellectual Property Generating Firms • A competitor just bought a new company with an exact copy of our stack?
- 7. Where did it go wrong? • Spoofing and Identity Theft • Gap in Capabilities between attackers and defenses • Security versus scalability myth
- 8. Specific Issues to Address • Due diligence for legal battles on specific breaches or illicit access • Inability to detect intrusions • Excessive trust in identities in ‘restricted environments’ • Need to solve these without performance hits
- 9. Nice to haves • Did my data set linking actually work? • Did this new analytic tool produce ‘quality’ results? • What questions can I ask? • Has my sensor array(s) just fried into garbage output? • Is someone tampering with my input data?
- 10. Potential Solutions • Option A – extend the TCP/IP stack with a security layer • Option B – rebuild the entire stack from the ground up • Option C – There is no C
- 11. Example B: Project Moonstone • A set of design and project planning docs - not code • Designed to provide security capabilities as a framework • Replace your Hadoop, Spark, and other data science systems • Adapters that allow these systems to operate inside Moonstone • Inside a pre-built SCRUM project framework • Little overhead required • Integrated modular distributed anti-virus and intrusion detection
- 12. Qu Secure Data Science Language Concept • Primitives to build other systems from built in graph analysis / SQL • Derived from Scala and Erlang • Security everywhere – no trusted places • Auditing guarantees
- 13. Qu Concept Overview • DataSet contain Table which are collections of Node • Node contain Links to other Node in the same Table or not • All are immutable – they can not change once created • DataSet control access to DataStore that load and create DataSet • All versions of all data stored, but some are offloaded to bulk storage • All DataSet, Table, Node, and Link have timestamps for creation
- 14. Moonstone on Qu • Identity Graph, 3 connectedness, and the SecureSocket Interface • Data cleaning as a security module • ‘System Temperature’ and automated intrusion reactions • Automated evaluations and auditing interfaces • Detecting perimeter threats