Artificial Intelligence (AI) has ventured into commercial reality because of high-speed computing and infinite storage resources, connectivity and bandwidth and access to unstructured and structured data. According to reports, the volume of business data worldwide doubles every 1.2 years. The rise in data sets helped AI practitioners to produce advanced and sophisticated algorithms to train machines and make them smarter to work in collaboration with human minds.
Big Data and cloud are conventional technologies and are driving AI revolution. AI is one of the largest single technological revolution worlds will see since the Industrial revolution almost 200 years ago. Data is the next natural resource, just like air and water. As per the Gartner, AI will be one of the top five investment priorities for more than 30 percent of CIOs globally. Whereas, with AI getting virtually pervasive in top strategic initiatives for IT have also changed. According to the World Quality Report 2017-18, the concerns around enhanced security and end-user experience are the top two aspects of IT strategy. Data security has been imbibed as a core tenet of IT quality. There is a rise in an increase of unstructured data collected by enterprises and IT departments are under continuous pressure to retain the data for longer periods and maintaining its security and privacy.
The recent case of Cambridge Analytica using Facebook data to manipulate the outcome of the 2016 U.S. presidential election and the Brexit vote has heightened concerns around the data security and privacy. Mapping, Moving and Managing data with the protection of private and confidential information is one of the major challenges for business to protect external as well their own internal data.
The challenges in data security and privacy
There’s a complete shift in the amount of data-generation and data management. Data is no more seen as a by-product of the business processes but it acts as a catalyst in supporting major business decisions. Albeit, such advancements in data management and warehousing, security mechanisms around data storage and data management has not been updated. This is by far, the most climacteric challenge in data security and privacy is widely prevalent former security mechanisms.
For instance, while developing AI solutions there are chances of generating more data or faster processing but the provenance of data is not clear in the cloud or in-house infrastructure. This leaves a gap for cyber frauds to sneak peek into business data from outside.
AI technologies have become mainstream with the coupling of Big Data and cloud environments. The real-time processing of data requires varied hardware and software infrastructures for providing unlimited storage and computing capacity on data have given way to new security challenges and concerns.
The vulnerability of data security increases with the variety and volume of data sources. The more the number of sources from where data is collected, it is hard to identify that the data source is malicious or not. With the adoption of cloud-based platforms, the chances of compromising data security and privacy have increased multi-fold.
Developing Big Data platform requires development teams to collaborate data sources from different sources and computing on high-computing platforms over diverse networks for faster results. The applications in internal IT infrastructure are being viewed at low risk, while applications placed in the cloud are prone to data breaches. Lack of data security measures can cost great financial and goodwill losses to any company and should be taken up with utmost care.
An AI driven Approach
The clear way of addressing these challenges is talent and technology. Companies can hire right set of skilled people or reskill existing staff to help in overcoming a gap of cybersecurity professionals. There’s already a dearth of data experts according to the existing demands and too few of them are working to address security shortcomings.
The data security and privacy concerns brought by latest technologies like IoT, Big data and Cloud can be addressed by AI. AI can enable systematic probing of past data available in cyber-attacks, frauds, and anomalies in data sets coming from varied sources. Companies like DeepInstinct, an AI company working in the areas of cybersecurity with technologies like Deep Learning, Data Training, and Predictive Analytics has achieved success in identifying malware files with a slight variation of 2% to 10%. This shows that advanced algorithms and data models can help in identifying potential data threats and eliminating them before confidential data being compromised.
There is a lot of momentum around security, access control, compression, encryption and compliance with data security and privacy. Coupling cybersecurity and privacy with AI can deliver results in a systematic way to make computing environments more secure to be shared over the cloud.
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