Sublime

PHR/EHR systems with adequate privacy and security controls can mitigate these risks, but most health staff are not trained well enough on the software to understand how to filter data carefully. Over the next few years, as health data generally becomes more liquid, we’re likely to see more and more slip-ups that are attributable not to the technol

The first two concerns I have above are technical in nature. In other words, something happened to the system that it wasn’t designed for. A company got hacked. An attacker emulated a real user and got access to their DA without authorization.

But the one that’s even more scary is when that doesn’t happen, and things work exactly as they’re supposed

primary among them is to detect and respond to threats.

We might also define an “Enterprise Attack Surface” that not only consists of all systems and networks in that organization but also the exposure of third parties. This includes everyone in the enterprise “ecosystem” including major customers, vendors, and perhaps government agencies. (Recall that in the case of the Target breach, the exploit came

a vendor for a $200,000 project to do customer data mining and another vendor to plug into all our POS systems to get sales data for customer analytics. “The first problem is that both projects violate the data privacy policy that we’ve given our customers,” John says. “We repeatedly promise that we will not share data with partners. Whether we cha

Attackers might be interested in learning information about the training data (resulting in DATA PRIVACY attacks) or about the ML model (resulting in MODEL PRIVACY attacks). The attacker could have different objectives for compromising the privacy of training data, such as DATA RECONSTRUCTION [89] (inferring content or features of training data), M