Skip to Content

Sponsored

Uncategorized

Diversity of Big Data Sources Creates Big Security Challenges

Produced in partnership withOracle

According to Oracle’s Neil Mendelson, many companies today make a key mistake in setting up their big data environments. “In an effort to gain insights and drive business growth, companies can too often overlook or underestimate the challenge of securing information in a new and unfamiliar environment,” says Mendelson, vice president for big data and advanced analytics at Oracle. That lack of attention to big data security requirements can, of course, leave the organization open to attacks from any number of unknown sources. 

Other evolving circumstances also contribute to a wide range of security-related risks, hurdles, and potential pitfalls associated with big data. As the Cloud Security Alliance, an industry group, notes: “Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition, and high-volume inter-cloud migration all create unique security vulnerabilities.”

Two additional complicating factors include: 

Related articles:

  • • Big Data, Bigger Responsibility

Outdated approaches. Previous perimeter-based approaches to security are simply no longer sufficient. A CSO Market Pulse survey found that “two-thirds of security budgets are used to protect the network, with less than a third used to directly protect the data and intellectual property that reside inside the organization.”

Insufficient governance. Forty-four percent of organizations have no formal data governance policy, and 22 percent of these companies have no plans to implement one, according to the 2013 Rand Secure Archive Data Governance Survey. Big data increases companies’ data ingestion by many orders of magnitude, adding to the complexity. Without overall management of the availability, usability, integrity, and security of big data employed in an enterprise, organizations will find it tough to address the mandates called for the U.S. Federal Trade Commission and the European Union.

Manage enormous volumes of data using scalable, distributed solutions to secure data stores and enable efficient audits and data provenance.

Securing the big data life cycle requires that organizations address four overarching areas, according to the CSA’s Big Data Working Group:

Infrastructure security. Secure computations in distributed programming frameworks as well as in nonrelational data stores.

Data privacy. Secure the data itself using a privacy-preserving approach for data mining and analytics. Also, protect sensitive data through the use of cryptographically enforced data-centric security and granular access control.

Data management. Manage enormous volumes of data using scalable, distributed solutions to secure data stores and enable efficient audits and data provenance.

Integrity and reactive security. Use endpoint validation and filtering to check the integrity of streaming data, and real-time security monitoring and analytics to help prevent and address security problems.

Bottom line: “Organizations today require not only the right manpower, but a comprehensive set of policies, procedures, and technologies to responsibility guard sensitive information,” Mendelson says. “Unlike in the past, all these resources need to be continually tested, reviewed, and updated.”

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.