The real antidote? Human control

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Attackers who want to compromise the integrity of ML outputs are already active, and their methods for compromising data are extremely sophisticated. Companies must pay close attention to these four points if they value the integrity of their ML models. However, one of the most effective ways to prevent such attacks is to ensure that the entire ML process is under human control. To prevent biased output, intelligent machines and humans must work together. Ultimately, this will thwart cutting-edge data manipulation attempts.

Data Management to Secure the Agile Enterprise
The “new normal” requires new data management policies. Fredrik Forslund, vice president of enterprise and cloud data cleansing solutions at Blancco, discusses for Information Age what aspects an agile enterprise needs to consider .

Agility has long been a fundamental characteristic of the modern enterprise. Since the pandemic hit, this has been tested, and businesses have had to adapt in ways they have not done before. Connectivity and the cloud have played a major role in enabling employees to work from anywhere, anytime, and on any device, and operations increasingly rely on massive amounts of data to help inform strategic decisions.

While these large volumes of complex data help israel mobile database respond quickly in challenging environments, they can be costly if not managed properly. Businesses should implement security solutions to protect sensitive and confidential information as standard, but research by CybSafe has found that around 90% of data breaches can be attributed to human error, which in turn exposes businesses to potentially high regulatory fines and reputational damage.

The pandemic has led to an increase in the use of home devices and networks, and companies have quickly made changes to accommodate a remote work model. This cultural shift has also created challenges for the agile enterprise.

Managing data in a remote environment
According to research from Armis, 44% of employees currently use their own laptops for work. The use of personal devices by a distributed workforce at this scale means that businesses need to rethink their data management and security policies. In many cases, they are not training their employees to properly secure their devices when accessing sensitive business information outside the workplace, or to manage data at the end of their life.