Immuta Predictions 2018: Risk, AI and The Future of Analytics

McKinsey estimates that tech giants invested between $20 to $30 billion in artificial intelligence in 2016, showcasing the increased adoption of AI across industries. Algorithmic-driven processes can sift through more data more effectively than any human mind could – presenting unique opportunities in different sectors to drive revolutionary results.

But serious pitfalls also coincide with these new opportunities. For example, AI presents significant risks driven by a lack of transparency around algorithm design, overall complexity, inappropriate use and weak governance.

These specific inadequacies can leave enterprises exposed and subject to harmful risks, such as undetected bias in decision making, computational errors, or the potential for malicious acts – mistakes that can be costly (and possibly devastating) when compounded over time.

It’s critical that organizations deploy solutions that mitigate the risks associated with machine learning models to establish trust and understanding into how these advanced technologies are making decisions.

The first step to being successful in today’s algorithm-driven sectors is to look at how a company’s data management platform is collecting and processing datasets before they’re fed into algorithms to ensure compliance with regulations that are on the horizon. It won’t be long before more governing bodies around the world pass data protection laws like GDPR – and to guarantee companies are prepared, it’s critical to treat all data as if it’s already regulated.

At Immuta, we believe that the right combination of sophisticated data policies will enable organizations to maximize and properly leverage the advanced analytics tools used to enhance different business functions. This is why our platform comes equipped with the following capabilities to protect organizations from the ramifications of non-compliance:

Automated differential privacy: Differential privacy allows organizations to extract maximum value from large data sets, while mathematically guaranteeing personally identifiable information can’t be compromised – giving data scientists access to completely anonymized data and ensuring compliance with even the strictest regulations.

Purpose-based restrictions on data: By dynamically enforcing data access and policy restrictions based on a data scientist’s needs in real-time, Immuta gives enterprises complete visibility into how and why different data sets are being used.

Personalized data access: By providing data scientists with data based on their roles, corporate policies and industry regulations, Immuta enables companies to accelerate the development of machine learning and AI algorithms with the confidence that all internal and external rules are automatically applied.

The most successful algorithmic-driven enterprises require the tools to adapt to consumer expectations, evolving legislation, and the potential pitfalls of the perceived misuse of data. Using the techniques provided by Immuta, organizations can better manage their algorithmic risk – while still taking full advantage of their data.

Learn more about the Immuta platform by downloading our new technical whitepaper here.