Is AI The Ultimate Answer For Cloud Security? Let’s Know More
Is AI The Ultimate Answer For Cloud Security? Let’s Know More
In the 21st century, we’re surrounded by activities that require cloud computing. Don’t believe me? Your social media accounts like Facebook and Instagram use the cloud. What happens when you upload a photo? It is stored in the cloud. Love binge-watching Netflix in your free time? The only way you are being able to access your favourite shows and movies is because of the cloud. In short, let’s just say mostly everything on the internet is running on cloud computing, and it shows no signs of slowing down. According to Rightscale’s 2018 report, 96% of companies are using the cloud to conduct some of their operations.
What’s the issue, you ask? Well, just like everything, this too comes with perils.
Cloud computing is vulnerable to breaches. Many IT professionals highlight that despite the growth of cloud computing, it’s the most vulnerable area within the business. To tackle this problem, many companies have started to rely on artificial intelligence and machine learning to strengthen their cloud security. Additionally, they are increasing their cloud security budgets.
How will AI help to combat this vulnerability?
Artificial Intelligence is a program that can think by itself and solve problems, just like us humans. Machine learning is a part of AI that uses algorithms to learn from several units of data. The more data it has to analyze, the more it learns based on the patterns to give useful insights.
While it’s not 100% bulletproof, AI and machine learning can be used to detect threats in real-time.
If you are a business using cloud computing, here’s how these technologies can benefit your security strategy.
Big Data Processing
If you employ a team to sift through several GBs of data that cybersecurity systems produce, it’s going to be impossible to deal with the massive data. While we can’t do it, machine learning technologies can churn that data to detect potential threats. The more it processes, the more it understands patterns for better detection.
Threat Prediction
Artificial Intelligence uses an advanced data-driven approach that can detect and alert vulnerabilities that are present at the time or might happen soon. This is calculated by observing the data coming in and out of protected endpoints. With sufficient data, this approach can detect threats based on known behavior and predictive analysis.
Threat Detection And Blocking
What will happen if there is a breach and the systems team is not present in the office to take immediate action? AI and machine learning will do it on their own. When these systems find glitches in the data patterns, they will alert a human or take immediate action by shutting a system under threat down. This gives businesses warnings about potential threats days in advance or detects and blocks a dangerous code within hours.
Self-handling Security Analysis
One of the advantages of employing an automated technology like AI and machine learning for cloud security is that it will handle first-level security threats on its own, giving you more time to focus on other complex issues. This means companies can rely less on manpower and use their potential elsewhere by delegating first-level analysis to bots.
While it might not solve every problem, AI will sift through massive amounts of data and analyze patterns to detect an anomaly. While AI and machine learning might respond to first-level threats on their own, that day is still far when it will take care of the breach on its own. Nevertheless, it will help you scatter through bundles of incoming data and warn you of any potential breach, keeping you one step ahead.
Share This Article
Do the sharing thingy
Published at Thu, 04 Feb 2021 04:07:30 +0000
She was named one of the 100 most brilliant women in AI ethics
Computer science professor Tina Eliassi-Rad says she’s proud to be named on an industry list of “100 Brilliant Women in AI Ethics,” which identifies her as one of the top thinkers in the male-dominated field of artificial intelligence. But she’s even prouder of what the carefully-curated list represents.
“Part of the issue in a field such as computer science is that women and other under-represented minorities aren’t always seen. Initiatives like this one show that there are a lot of women who are qualified to do this work,” says Eliassi-Rad.
Mia Shah-Dand, the CEO of the Oakland, California-based research firm Lighthouse3, created the annual list in 2018. Shah-Dand says she wanted to provide a rebuttal to technology leaders who complained that they couldn’t find accomplished, diverse women to hire.
“I was a little frustrated with all the times I would hear, ‘There just aren’t enough qualified women,’” says Shah-Dand. “It’s the same old excuse. Well, we have an entire directory of qualified women now. There is no excuse. At this point in 2021, if you have only men on your staff, it’s intentional.”
According to recent research by the World Economic Forum, women hold only 26% of data and artificial intelligence jobs across the globe, and even fewer have senior roles.
Shah-Dand says she included Eliassi-Rad on her 2021 list because of the professor’s extensive research on racial, gender and other baked-in biases in artificial intelligence algorithms.
“Her emphasis on algorithmic accountability and fairness was particularly interesting,” says Shah-Dand.
Algorithms, which scan large amounts of data and find whatever information its creators want, are increasingly part of our everyday lives. For example, credit card fraud departments use algorithms to detect abnormal spending, while social media algorithms use viewer interests to determine which ads to run.
Eliasi-Rad’s research at Northeastern focuses on the unseen but overwhelming influence that artificial intelligence algorithms can make in people’s lives, especially in social media.
“Part of the problem with algorithms is that they can impact life-altering decisions if they’re used in criminal justice or even your credit score,” says Eliassi-Rad. Microlenders, or individuals who issue small loans, will often check a candidate’s Facebook and Twitter feeds when deciding whether to grant a loan. A chance connection with someone who has defaulted on a loan could trigger a denial, says Eliassa-Rad.
“Sometimes if you don’t get the right loan in life, you can’t better yourself,” she says.
Eliassi-Rad’s career in computer science was sparked by her father’s early work with autonomous vehicles. She avidly read the many magazines he brought home and decided computer science was the perfect balance between math and electrical engineering. Her focus recently sharpened as she learned about the different class, race, and gender biases in machine learning.
She likens the data used in algorithms to an iconic photo of a police officer’s German shepherd attacking a Black high school student during a 1963 civil rights event in Birmingham, Alabama.
“The German shepherd isn’t racist, it’s the people teaching the dog,” Eliassa-Rad says. Even if the data used in an algorithm isn’t biased, the algorithm may still produce biased findings.
“As you are developing an algorithm you are making choices, and those choices have consequences,” Eliassi-Rad says.
Eliassi-Rad and Shah-Dand say the list of top women in AI ethics does more than provide a roster of qualified computer science professionals who also happen to be female, LGTBQ, or women of color. It creates a community to foster networking and support while providing role models for future generations.
“It’s sort of like a sisterhood,” says Eliassi-Rad, who received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010. “I hope young women see this and think, ‘I can be somebody like this person.’”
For media inquiries, please contact media@northeastern.edu.
Published at Thu, 04 Feb 2021 02:26:02 +0000
You must be logged in to post a comment.