As security concerns grow, the economy will require a new approach to data privacy that maximizes user benefits while minimizing the inconvenience of new technologies. Organizations will need to think more clearly about how they store and track customer data, take appropriate measures to stress test their systems, assess risks in real time, and minimize damage.
Thousands of business leaders around the world consider data security and privacy issues to be the most serious obstacle to successful digital transformation. 74% of respondents named privacy as an urgent societal issue, followed by information security (73%).
The new economy will require new questions about how to interact with intelligent machines as they play increasingly diverse roles in our economy. These questions go far beyond the familiar architecture of security and interactions. The emergence of semi-autonomous cars is a case in point. There are many nuances to consider when designing human-machine interactions.
A recent Uber patent is a case in point. When filing a indonesia whatsapp data for a self-driving car that replaces a human driver with a hologram, the company decided to have the hologram look pedestrians in the eye (as a human driver would), signaling them that it’s safe to cross the street. However, the consequences of getting that signal wrong could be significant and potentially deadly.
Trust and transparency
People and organizations routinely and deliberately create complexity to avoid putting themselves at risk, from simple security badges that only allow employees to access to more complex technical barriers to accessing data. If we remove this complexity, the risk or perception of risk increases. Given growing trust issues, this could be a difficult situation.