Data providers, healthcare institutions, banks, and government have the need to prevent unintentional loss of private or sensitive data, but their core business requires them exchange, or even sell, private or sensitive data - a process completely counter to data loss prevention (DLP). While not the core issue they faced, the Equifax breach highlights this DLP challenge. The vulnerability exposed Equifax to a massive data exfiltration through an "exit" from which they would normally expect sensitive information to leave. Current tools do not help enterprises that need to "break the rules" and push protected data externally - a process for which they were designed to counter. A system that prevents accidental and intentional release of enterprise sensitive data can't effectively guard the gateway where the largest amount of sensitive data enters and leaves the enterprise. Public and private data providers are critical to help business succeed, but they also need to know how to more effectively mitigate improper exfiltration of data through the very gateways they need to serve their customers and business partners.
IoT is H-O-T! But, it is still at its nascency. The "things" in IoT vary based upon domain, environment, and context, and we are only in our earliest days understanding where they can be applied to reducing risk in identity management. This blog will pull together the elements needed for industry to be able to use IoT across channels and domains. You will see the greatest challenges in provisioning devices to individuals. Next, you will get to see my simplified view of the lifecycle of IoT devices and how it impacts provisioning. Finally, it will describe the art of disambiguation, without giving away too many secrets, as the crux of using IoT in the world of identity management. Bottom line, we have an opportunity to look at IoT as not a confusing array of gadgets, but a better model to serve our users, while also increasing the integrity of the transaction without as much customer friction.
Live by my simple adage in cybersecurity, "Machines Don't Do Bad Things, People Do." When you look the potential vectors of cyber, physical, and personnel threats: the vulnerabilities, the mistakes, and the attacks, can all be traced back to a person. Using this adage in building a cyber defense strategy, provides a new kind of framework to measure and reduce threats. The challenge: even though you may see a machine going awry, it is really, really hard to find the "bad guy" before the vulnerability is exploited or the attack is in play. So, in an effort to come at this problem a new way, let's examine "Brent's Inverted Corollary of Cybersecurity" (breaking news), "Machines Don't Do Good Things, People Do".