Is Your Quest for Artificial Intelligence Risking Your Customer Privacy

 In Analytics, APIs, Artificial Intelligence, AutoAI, autolearn.ai, Big Data, Blockchain, DataStack, Digital Strategy, InnovationLab, LearnCloud, Technology & Design

Artificial Intelligence holds great promise in its ability to uncover trends and patterns that can be used to make products and services more intelligent, predictive and prescriptive. However, building AI enabled systems require deep, flexible and broad access to all data. Because generating AI is a complex, iterative process that requires access to all primary and tertiary data related to a domain, data scientists often prefer casting a wide net that enables them to iterate to determine which data signals hold the most value.

This poses an interesting problem for the enterprise. Since most enterprises either build an in-house team of data scientists or leverage data science capabilities of vendors and service providers, enabling access to data becomes a big deal and a potential roadblock. Enterprises have to determine what, how and when to enable access to data and how to govern and manage the access on an ongoing basis.

Even if the master copy of the data is stored securely, either encrypted or protected or both, almost always it needs to be copied to an “analytical” system or exposed through specific analytical tools in an unencrypted form. Regardless of an in-house data science team or an external vendor team, the data needs to move outside its primary storage location. This makes enabling AI team to be productive tricky.

Data Governance

Enterprises need to ensure that they are able to determine, at any point in time, who has access to what data and what are they doing with it. In addition, the enterprise needs to ensure that they track the usage of data by specific employees (full time or part time) or system/applications and can determine the entire usage lifecycle. In addition, if need be, the enterprise should be able to terminate access or restrict access as needed.

Data Security In Motion

Enterprises should ensure that data when transferred between systems is transferred over a secure channel and is encrypted as it is being transferred. Data transfer can be protected through the use of transfer protocols such as SSL/TLS or HTTPs. Data should also be encrypted while it is being transferred between stores

Data Security at Rest

Another key requirement is to ensure that all data is encrypted when at rest. This means that wherever the data is stored, it should be stored in an encrypted form to ensure that any unauthorized access to the data does not reveal any private information about what the data represents.

Data Security During and Post Analysis

Often. the most overlooked aspect of data security is the security of the data during and post analysis. During analysis, the data set can be transformed, copied, modified or pivoted. This leads to many different “copies” of the data being created. Ensuring that the original copy of the data is protected and secured is not enough as enterprises need to ensure that any analytical copies created are tracked and subsequently protected or removed. 

Data Security of AI Models

The output of AI i.e. models that can predict/classify also need to be protected as they become the enterprise’s IP and can provide insights into how the enterprise’s products and services will behave when the models are integrated. These models need to be stored encrypted and secured behind authentication, authorization and appropriately discarded when no longer relevant.

Building a secure AI practice is easier said than done and very few vendors offer this service. The AI Company has a suite of products that together enable the fastest, simplest and the most secure AI experience for the enterprise. Our security is comprehensive and stands out in comparison to any other offering in this space. Talk to us to see how you can ensure that your AI investments don’t leak your or your customer’s private information and IP.

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