Attackers who want to compromise the integrity

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Use open data with caution. It is very attractive because it provides access to more information to enrich existing sources. In principle, this should make it easier to develop more accurate models. However, openness is not only their strength, but also their weakness. It makes them an easy target for scammers and hackers. The recent attack on the PyPI repository, which flooded it with spam packages, shows how easy this can be.
The real antidote? Human control
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
Sergey Stelmakh | 10/15/2021
Fredrik Forslund
Fredrik Forslund
The “new normal” requires new data dominican republic mobile database 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.