With 80 per cent of company data being unstructured, including text, images and video, getting the most possible value from rising amounts of these assets is proving a challenge across all business sectors. Businesses often meet pitfalls in keyword search capabilities that fail to properly take context, formats or languages into account, leaving users with insufficient results. To solve this challenge, Barcelona-headquartered data startup Nuclia is delivering an API that leverages what company CEO and co-founder Eudald Camprubi has named'AI search as a service', capable of finding and indexing data across any source. An end-to-end solution, it can extract data from file repositories, audio, video, URLs and databases, split it into paragraphs, and present an index that shows exactly where any chosen piece of information is in the file. This is based on continuously trained language models, the creation of which owes much to data annotation.

As the attack surfaces of organisations has grown since lockdown took hold and employees migrated to remote devices, pre-pandemic data protection practices have since needed a revamp of methods. Increasingly, this has entailed the automation of processes, powered by technologies such as artificial intelligence (AI), which is capable of making data protection more efficient if implemented properly. With this in mind, we take a look at some of the most valuable ways in which artificial intelligence lends itself towards data protection. This article will explore how artificial intelligence is set to impact organisations in the future, gauging the insights of experts in the space. Because AI relies heavily on data algorithms, organisations may feel hesitant about using the technology to aid data protection practices.

We realize that Machine Learning is the most famous field in the present age, it is being acknowledged and executed in different enterprises particularly in medical care, ML is performing astoundingly in the clinical field by distinguishing malignant cells in patients at the soonest stage and foreseeing future pandemics or danger flare-up in earlier so the administration could take prudent techniques. Indeed, even to this point, the ML algorithm is helping mankind by contributing its potential in anticipating Covid defilement and in discovering antibodies against the infection. The entirety of this causes Machine Learning to be attractive because we realize that this innovation will be the essence of a future digitalized world. Security Automation is a part of ML which is one among every one of the things that cause us to accept a profession in ML will be emphatically gotten. Security computerization is demonstrating to be the following large thing, later on, after thinking about it you will acknowledge how much significant it is to turn into a piece of it.

Artificial Intelligence and Automation should be used in cyber threat detection to increase security, efficiency and help organizations be pro-active, helping them see the threats in advance and keep their infrastructure and data safe. As organizations dwell into smarter and innovative products, they are dependent on critical data which is under constant threat. A breach of critical data can put the organization and its customers at serious risk. A combination of AI and Automation can be leveraged to counter these threats and provide insight into obscure and malicious activity on systems, networks, and infrastructure. In 2017, the average number of breached records by country was 24,089.

Yet, for the first line of defense in an enterprise environment, the analysts working in the security operations center (SOC), the notion of automation is more headline than reality. Many basic tasks – logging, fault isolation, reporting, and incident troubleshooting – are still very much manual. Often monitoring up to tens of thousands of alerts an hour, it is a tough problem that presents a constant battle of humans versus events and that's why the right automation is so important. Unfortunately, the automation tools available today only scratch the surface of addressing the biggest challenges that security teams face. Decision automation is a different class of security automation. It emulates human reasoning and decision-making skills, within the context of the environment, to reduce the high volume of noise, which usually produces many false positives.

In the past few years, there's been a lot of buzz around artificial intelligence (AI) in cybersecurity. Can AI really help businesses improve their security posture? How can we determine which solutions actually use AI versus which ones make hyped-up claims? For solutions that can help, how do they help? Obtaining clarity around this subject will help us understand the areas in which AI can help and what value it can add, which will, in turn, help us make more informed decisions.

Businesses of all sizes continually seek ways to increase efficiency and profitability in all areas of their organization -- everything from general operations to cyber security. Regardless of how you feel about automation on a personal level -- whether you think automation is great or it's the harbinger of death for cyber security jobs -- it doesn't change the fact that automation is poised to change the very nature of cyber security jobs in the future. That's because one of the best ways to accomplish many of the goals business have is to integrate process automation and cyber security automation into their operations. Business automation comes in many forms, though, and can include a variety of process automation and security automation tools. So, what are these tools, how do they work, and how can they be integrated into your security processes?

In this Spotlight Podcast, we speak with David Brumley, the Chief Executive Officer at the security firm ForAllSecure* and a professor of Computer Science at Carnegie Mellon University. Brumley is a noted expert on the use of machine learning and automation to cyber security problems. In this podcast, we talk about the growing demand for security automation tools and how the chronic cyber security talent shortage in North America and elsewhere is driving investment in automation. Every so often, a technology comes along that seems to perfectly capture the zeitgeist: representing all that is both promising and troubling about the future. In the 1960s, you think of plastic, which was a pillar of a massively expanding consumer culture in the United States that put "convenience" above all else.

Cybersecurity and artificial intelligence now seem to go hand in hand as complementary weapons against crime, but what lies ahead? Manish Bahl is the senior director for Cognizant's Centre for the Future of Work. He is responsible for thought leadership around code halos, digital transformation and the future of work. He works with IT and business decision-makers to provide vision on digital transformation and its effects on business, people, technology and culture. "Cognizant's Centre for the Future of Work is a dynamic thought leadership powerhouse that examines how work is changing, and will change, in response to the emergence of new technologies, new business practices, and new workers," he explains.

Artificial Intelligence and Automation are being used in multiple industries to make the lives of people better and to make things more efficient for organizations. AI is being used to diagnose medical conditions or offer legal advice. Automation is being used in factories to reduce reliance on manual labor and to improve the quality of products. As AI and Automation are becoming more universally applicable concepts and can be implemented irrespective of the industry. These innovative technologies depend on an organization's critical data that today is threatened by various factors.