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Security considerations for the AI era

Analytics often involves corporate data and proprietary training algorithms. Keep them out of the wrong hands with these key strategies.

IT pros who are working with machine learning or artificial intelligence deployments cannot overlook the importance of security. AI models can be built with the capability to access billions of data points within a business unit or an entire organization - and many incorporate third party data feeds as well. This type of deep learning may put a company at risk.

What do you need to consider to make AI development more secure?

  • Secure the code. Code should be designed in a way to prevent unauthorized access. Machine learning is adaptable, so algorithms can be written to reduce risk. AI can also learn the environment and application well enough so that it can detect when changes occur or are inserted.
  • Secure the environment. Build and develop code within a secure infrastructure, where data and access are locked down.
  • Understand the risks. Understanding the potential threats allows you to develop and implement changes to secure your AI-based applications.
  • Detect problems quickly. Utilize an environment that allows you to centralize and monitor all activities and access. An integrated environment allows you to find and eliminate threats fast.
  • Encrypt data at rest and in motion. The ability to encrypt the code and data will keep applications more secure.

Microsoft Azure Security Center, for one example, presents a deep-dive into all of the resources that you may be working with in the Microsoft Cloud.

Security Center enables you to:

  • Quickly retrieve information on the security of all of the Azure assets.
  • Create strategy-driven security setups.
  • Maintain compliance with Microsoft policies.
  • Discover risks by using machine language-driven examinations of the environment and data.
  • Provide an immediate response to at-risk areas within Azure.

With Microsoft Azure Security Center, you can create subscription-based policies to secure your environment. As the administrator, you can implement stricter rules within production (as opposed to dev areas) to keep your deployed code and applications secure.

Microsoft Azure delivers the following methods to secure your applications within the cloud:

  • Key Vault encrypts passwords to production applications and stores keys securely.
  • Virtual Machines provide a secure environment in which to store, develop and run your applications.
  • Multi-Factor Authentication lets you add an additional layer of security to ensure access control and role-based privileges.
  • Microsoft Azure Status provides up to the minute status regarding the security of all of your cloud-based environments.

Microsoft Azure has added a layer of security by partnering with dedicated security vendors such as Trend Micro, GreatHorn, and CheckPoint. Bottom line: Microsoft Azure Cloud provides numerous options for securing your AI applications.

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