机器学习风险评估_5种常见的机器学习安全风险以及如何克服它们

机器学习风险评估

I still remember the days when the software development industry was in its infancy. Many people were concerned about software vulnerabilities and exploits, and they were right back then as hackers took advantage of these exploits and started fulfilling their malicious designs. Every data breach and cyber-security attack was extensively covered by mainstream media, both print and electronic.

我仍然记得软件开发行业处于起步阶段的日子。 许多人担心软件的漏洞和漏洞利用，而当黑客利用这些漏洞并开始执行其恶意设计时，他们就在那时。 主流媒体(印刷媒体和电子媒体)广泛地涵盖了每次数据泄露和网络安全攻击 。

The focus is more on purging individual bugs than identifying the root cause of the problems. A few years later, we realized that the only solution would be to build secure software. A few decades later, software security has become an integral part of cyber-security programs.

重点更多是清除单个错误，而不是找出问题的根本原因。 几年后，我们意识到唯一的解决方案是构建安全软件。 几十年后，软件安全性已成为网络安全程序不可或缺的一部分。

With today’s software and apps using machine learning and artificial intelligence, it is important to secure machine learning and artificial intelligence systems you are using. Don’t get me wrong machine learning can do a much better job than humans at tasks such as image classification, translation, play and win complex games such as chess, Go along with other video games.

使用当今使用机器学习和人工智能的软件和应用程序，确保所使用的机器学习和人工智能系统的安全非常重要。 别误会，机器学习在诸如图像分类，翻译，游戏和赢得复杂游戏(如国际象棋，围棋以及其他视频游戏)的任务上可以比人类做得更好。

Despite its advantages, some businesses are still reluctant to use machine learning based systems due to security risks attached to them. If you adopt machine learning in a haphazard way, you are more likely to increase your security risk manifold. That is why i