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  1. Large amounts of data sets raise privacy concerns, but can you lock AI behind firewall?

Large amounts of data sets raise privacy concerns, but can you lock AI behind firewall?

The insane rate of growth in data has resulted in enormous amounts of data sets for organisations. In order to meet the present day needs of real-time business outcomes, meaningful analytics are the key differentiator to smarter decision-making and gaining competitive advantage in the market.

Published: January 29, 2018 4:46 AM
data privacy norms, data privacy concern, artificial intelligence cyber security, cyber security risks Organisations now need to both innovate on and safeguard critical data of employees, customers and business partners.

By-Harish Arora    

The insane rate of growth in data has resulted in enormous amounts of data sets for organisations. In order to meet the present day needs of real-time business outcomes, meaningful analytics are the key differentiator to smarter decision-making and gaining competitive advantage in the market.

Data is dynamic, distributed and diverse

Transformation is happening all across the industry, shifting from equipment focused power in the industrial age, to today, where data holds all the trump cards. Organisations now need to both innovate on and safeguard critical data of employees, customers and business partners. In the era of Big Data and globalised open innovation, this is both a challenge and an opportunity with data generated every day at an exponential rate.

The truth is that data has gone beyond staying behind the firewalls of devices. Data patterns are now enabling a viable, robust and thriving e-commerce environment by reducing application response time. Disruption is the name of the game, and a dynamic and efficient use of data chooses the victor.

When we talk about AI and data privacy, we are essentially referring to machine learning implemented on Big Data, also commonly called Big Data Analytics. Big Data Analytics has a lot of positive real-world effects. A most common example is the data generated in the transportation sector that can be used to reveal travel patterns across road networks. By identifying such patterns, services can be created to benefit travelers.

This data is what causes a concern because as individuals, we are not sure what is private and what isn’t today. In fact, a lot of this data is stored in the hybrid cloud. This means that data is forever moving. The secure movement of this data, which could be living in a public cloud or on a secure server, is critical.

Privacy by design: Breaking down the hype

Privacy by design is a policy framework through which privacy is proactively embedded into the design and operations of the IT systems, networked infrastructure and business practices. The root principle is based on ‘enabling service without data control transfer from the citizen (user) to the system’. A good example is GPS, which gives a location without giving away the identity of the user.

From a compliance standpoint, as an example, privacy by design is a new part of European Union regulations, contained in the GDPR or General Data Protection Regulation (that comes into force in May 2018) as well as the recent urge to adopt the ‘privacy by design’ framework when ‘right to privacy’ was elevated to a fundamental right under the Indian constitution. The concept was also in the news back in 2010, when over 120 different countries agreed that they should contemplate privacy in the build.

It is true that new businesses will face challenges with enormous amounts of data-streams, while looking for patterns that open up new business opportunities. What is required is the adoption of a comprehensive approach to data protection, which helps balance the need for data access with the need to protect that data from attackers. Companies deploying AI are well aware that the future success of this technology lies in ensuring security without a trust deficit.

How AI and privacy can co-exist

The need of the hour is to embed a framework of data privacy and data protection policies right at the source—into Big Data analysis so society as a whole can benefit from Big Data and AI without privacy concerns.

Data privacy encryption solutions keep private data private and help AI proponents meet regulatory compliance security mandates. For example, our data security solutions offer encryption without traditional compromises around performance, OS-dependence, and ease of use, and enable corporations to meet compliance security mandates contained in regulations such as the Payment Card Industry Data Security Standard (PCI DSS), HIPAA or such. External regulations and internal best practices point to data encryption as a key solution to data privacy. In the end, data integrity through integrated antivirus technology, cyber analytics, standardised encryption and centralised key management, will be the ultimate succour to ensure confidentiality for data at-rest or in-flight.

The writer is VP – Data Technologies & Fabric Enablement Group, NetApp India

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