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Preventing Predictable Problems (Possibly)

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Presentation given during a panel session on Innovation, Complexity, Risk and Trust at the MAPPING Second General Assembly in Prague, Czech Republic, on 1st November 2016.

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Preventing Predictable Problems (Possibly)

  1. 1. PREVENTING PREDICTABLE PROBLEMS (POSSIBLY) Gareth Niblett
  2. 2. PROBLEMS Problems, Problems
  3. 3. BADTHINGS CAN HAPPEN ACTION • ‘Wise Monkeys’ approach • Vulnerability disclosure • Service failure / denial • Data leak / breach • Data destruction REACTION • Increased costs • Recall / reputation damage • Fine / loss of license • Loss of revenue / value • Job losses / business closure
  4. 4. OPPORTUNITIES Optimism & Options
  5. 5. PLANTO WIN • Solve a problem / innovate • Think ahead • Listen to experts • Prepare for failure • You can’t predict it all
  6. 6. BUILDTO SURVIVE • Assess risks honestly • Scale flexibly & efficiently • Built-in security, not bolt-on • Test resilience plans • Adapt and overcome issues
  7. 7. BE ‘UNWISE’ • Listen to customers, experts, and regulators • Speak (and ask) about concerns and problems • Look proactively for problems, and don't ignore Failure can be ‘fatal’
  8. 8. EASY PICKINGS • Follow standards and test • Use secure protocols • Avoid bad defaults • Make patchable & automatic • Don’t overburden users
  9. 9. INNOVATE SECURELY • Internet ofThings • Identity schemes • Surveillance tech • Augmented / virtual reality • Big data & analytics • Machine Learning / AI • Autonomous vehicles • Drones • Regulation & legislation • Blockchain
  10. 10. TECHNOLOGY TacklingThreats
  11. 11. INTERNET OFTHINGS • Use interoperable standards • Have on-device protection • Enable automatic updates • Manage external trust • Limit data collection & use
  12. 12. IDENTITY SCHEMES • Provide broad user benefits • Make it citizen/user-centric • Decentralised & federated • Trusted throughout lifecycle • Transparent and auditable
  13. 13. SURVEILLANCETECH • Necessary & proportionate • Minimise data & retention • Limit purposes & access • Oversight & accountability • Don’t be ‘evil’, or facilitate it
  14. 14. AUGMENTED REALITY • Tackle online abuse • Be fair with ads & targeting • Ensure data quality • Take care with location data • AR/VR use may be sensitive
  15. 15. BIG DATA & ANALYTICS • Limit scope / purpose • Be responsible and ethical • Understand anonymisation • Try prevent reidentification • Correct bad data & decisions
  16. 16. MACHINE LEARNING / AI • Address ethics properly • Minimise algorithm biases • Accept robots taking jobs • Secure user-derived learning • Avoid Skynet / singularity
  17. 17. AUTONOMOUSVEHICLES • Ensure secure connectivity • Address trolley problem • Get government support • Get insurance co backing • Leverage sensor data wisely
  18. 18. DRONES • Regulate for safety & privacy • Geo-fence for safety & security • Handle GPS spoofing / jamming • Risk-based registration/ license • Monitor misuse and respond
  19. 19. REGULATION & LEGISLATION • Keep it light touch • Limit strict / restrictive rules • Use to open opportunities • Status quos are not sacred • Accept always behind curve
  20. 20. BLOCKCHAIN • Use appropriately • Beware of trade-offs • Features can help, or bite • Regulators & users matter • It’s just another database
  21. 21. THOUGHTS ThinkingTime
  22. 22. SECURITY GIVES PRIVACY • False dichotomy begone • Remember Ben Franklin • Backdoors undermine us all • Design for privacy, by default • Build and operate securely
  23. 23. garethniblett.com @garethniblett Gareth Niblett

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