Managing the Threat Above—How AI Can Streamline Cloud Computing with Burgeoning Data Growth

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In this special guest feature, Tyrone Pike, President & CEO of FileShadow, Inc., believes the number of files transferred daily makes it extremely difficult to manage manually, but AI is improving cloud technology to handle the influx. Applying AI to cloud storage enables users to gather, analyze, catalog and protect more data than ever before. With more than 35 years of experience in the high-tech sector, Tyrone has held numerous top executive positions in software, software systems integration and networking infrastructure companies, including Solid Instance, Sparxent Inc., Bravara Communication, Inc. and InterNAP Network Services Corporate. He serves as the Chairman of the Board of Solid Instance, Inc., and holds an AB in Architecture from Princeton University.

Tech gurus are not surprised to hear that 2.5 quintillion bytes of data are generated every day. The role of cloud technology in how businesses and prosumers store and manage this data is enormous.

But because cloud technology has emerged only recently as a vital tool for businesses, and because businesses and individuals have dumped their data into cloud applications without a strategy (or have resisted moving to the cloud because they have no strategy), there are quite a few shortcomings. Many cloud applications have weak search capabilities. Human error, and the sheer amount of searchable data now being produced innately contributes to misplaced files across multiple cloud platforms. It’s easy to forget a filename, or where a file is stored, and PDF documents don’t always show up in a keyword search if there is no keyword in the filename.

The number of files transferred daily makes it extremely difficult to manage manually, but artificial intelligence (AI) is improving cloud technology to handle the influx. Applying AI to cloud storage enables users to gather, analyze, catalog and protect more data than ever before.

Here are a few examples of AI solutions in cloud computing:

Digital Asset Management

Take the aforementioned example of lost or hard-to-find files. AI is now smart enough to know more information about your files than you could ever find out manually. Technology from Google and IBM scans images to determine key characteristics, including image location and context. For example, you may have pictures of your sailing excursion in Boston, but you didn’t take the time to name or manually tag them. AI can examine each image and recognize pictures with sails or sailboats in them, along with other related characteristics such as ocean, boat, water, etc. It can then tag those images with all of those words for easy searching.

These same AI tools can also tag a file with the location where it was taken, making it much easier to find photos of “sailing in Boston” than manually scouring through your cloud storage to try to find the photos.

AI can perform optical character recognition (OCR) on PDF’s, allowing you to find a word in a document that hasn’t been indexed in a regular search engine. For example, you could find a specific term in a contract from the 1980s that referenced work you did with a client. Searching for the company name would yield instances of that company name in documents, even if the company’s name wasn’t included in the title.

And, these technologies aren’t treating your cloud data in silos. Services can now connect to and search in multiple cloud storage repositories, such as Box, Dropbox, Google Drive, Adobe Creative Cloud, OneDrive or another account.

Aerial Imagery

There’s no shortage of potential uses of machine learning and artificial intelligence in data science, namely in aerial imagery as a service. Also, cloud delivery enables easy access to an entire world of aerial captures from any device.

With high-resolution aerial maps in 2D and 3D, machine algorithms are now able to immediately detect stationary features on the ground without human interaction, such as roadways, buildings, swimming pools, solar panels, patios, parking lots, trees, etc. Applications include drone delivery, smart cities, autonomous driving, along with several engineering, construction and architecture use cases.

Virtual Assistant Development

Thanks to cloud computing and advances in machine learning, virtual assistants (VAs) created by Amazon, Google and Apple have quickly become a fixture in consumer homes. These companies are developing AI systems which can learn new words and how to carry on a conversation.

Cloud computing could be key in achieving this goal by storing the data which the AI accesses to respond to inquiries and learn new things. As AI learns, it can impart this new data back to the cloud, improving future AI as well.

A significant application of digital assistants is boosting workplace productivity and workflow. Speech-to-text can be used for email dictation and to transcribe meeting notes. Text-to-speech can be used to read written content aloud. Speech recognition can allow for conversational interactions and task management with the VA and “sentiment analysis” can detect overall morale in an enterprise through speech analytics.

Analytics and Business Intelligence

Companies can now use machine algorithms to identify insights in large data sets stored in the Cloud in real time so that decision-makers can determine trends, patterns, behaviors, and predict risk and potential outcomes to make recommendations. This has the potential to save time, money and optimize company logistics and resources.

Business intelligence (BI) dashboards like SAP, NetSuite, Oracle and Microsoft Dynamics-NAV can now process a much wider variety of data than before, so users catch insights that previously were lost in the data.

This predictive technology shifts the traditional role of BI from reactionary to proactive.


Last month, IBM Corp. launched IBM Security Connect which, according to the company, is “the first security cloud platform built on open technologies, with AI at its core, to analyze federated security data across previously unconnected tools and environments.”

IBM Security Connect allows vendors and developers to apply cloud technology, machine learning and AI to cybersecurity products to increase their effectiveness. Through predictive analytics, the platform detects suspicious behavior which will help cybersecurity staff who deal with threats on a daily basis.

Developments in cloud capabilities and AI will be interesting to watch because one will only be improved with the development of the other, guaranteeing infiltration in both our professional and personal lives.


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