Lucia Adams

AI Role Analysis in Cybersecurity Sector

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Cybersecurity as the name suggests is the process of safeguarding networks and programs from digital attacks. In today’s times, the world sustains on internet-connected systems that carry humungous data that is highly sensitive. Cyberthreats are on the rise with unscrupulous hackers taking over the entire industry by storm, with their unethical practices. This not only calls for more intense cyber security laws, but also the vigilance policies of the corporates, big and small, government as well as non-government; needs to be revisited.

With such huge responsibility being leveraged over the cyber-industry, more and more cyber-security enthusiasts are showing keen interest in the industry and its practices. In order to further the process of secured internet systems for all, unlike data sciences and other industries; the Cybersecurity industry has seen a workforce rattling its grey muscle with every surge they experience in cyber threats. Talking of AI impressions in Cybersecurity is still in its nascent stages of deployment as humans are capable of more; when assisted with the right set of tools.

Automatically detecting unknown workstations, servers, code repositories, and other hardware and software on the network are some of the tasks that could be easily managed by AI professionals, which were conducted manually by Cybersecurity folks. This leaves room for cybersecurity officials to focus on more urgent and critical tasks that need their urgent attention. Artificial intelligence can definitely do the leg work of processing and analyzing data in order to help inform human decision-making.

AI in cyber security is a powerful security tool for businesses. It is rapidly gaining its due share of trust among businesses for scaling cybersecurity. Statista, in a recent post, listed that in 2019, approximately 83% of organizations based in the US consider that without AI, their organization fails to deal with cyberattacks. AI-cyber security solutions can react faster to cyber security threats with more accuracy than any human. It can also free up cyber security professionals to focus on more critical tasks in the organization.

CHALLENGES FACED BY AI IN CYBER SECURITY

As it is said, “It takes a thief to catch a thief”. Being in its experimental stages, its cost could be an uninviting factor for many businesses. To counter the threats posed by cybercriminals, organizations ought to level up their internet security battle. Attacks backed by the organized crime syndicate with intentions to dismantle the online operations and damage the economy are the major threats this industry face today. AI is still mostly experimental and, in its infancy, hackers will find it much easy to carry out speedier, more advanced attacks. New-age automation-driven practices are sure to safeguard the crumbling internet security scenarios.

AI IN CYBER SECURITY AS A BOON

There are several advantageous reasons to embrace AI in cybersecurity. Some notable pros are listed below:

  •  Ability to process large volumes of data
    AI automates the creation of ML algorithms that can detect a wide range of cybersecurity threats emerging from spam emails, malicious websites, or shared files.
  • Greater adaptability
    Artificial intelligence is easily adaptable to contemporary IT trends with the ever-changing dynamics of the data available to businesses across sectors.
  • Early detection of novel cybersecurity risks
    AI-powered cybersecurity solutions can eliminate or mitigate the advanced hacking techniques to more extraordinary lengths.
  • Offers complete, real-time cybersecurity solutions
    Due to AI’s adaptive quality, artificial intelligence-driven cyber solutions can help businesses eliminate the added expenses of IT security professionals.
  • Wards off spam, phishing, and redundant computing procedures
    AI easily identifies suspicious and malicious emails to alert and protect your enterprise.

AI IN CYBERSECURITY AS A BANE

Alongside the advantages listed above, AI-powered cybersecurity solutions present a few drawbacks and challenges, such as:

  • AI benefits hackers
    Hackers can easily sneak into the data networks that are rendered vulnerable to exploitation.
  • Breach of privacy
    Stealing log-in details of the users and using them to commit cybercrimes, are deemed sensitive issues to the privacy of an entire organization.
  • Higher cost for talents
    The cost of creating an efficient talent pool is very high as AI-based technologies are in the nascent stage.
  • More data, more problems
    Entrusting our sensitive data to a third-party enterprise may lead to privacy violations.

AI-HUMAN MERGER IS THE SOLUTION

AI professionals backed with the best AI certifications in the world assist corporations of all sizes to leverage the maximum benefits of the AI skills that they bring along, for the larger benefit of the organization. Cybersecurity teams and AI systems cannot work in isolation. This communion is a huge step forward to leveraging maximum benefits for secured cybersecurity applications for organizations. Hence, this makes AI in cybersecurity a much-coveted aspect to render its offerings in the long run.

Madeline Miller

6 Best Podcasts On Big Data To Check Out

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Podcasts are one of the best ways to learn about big data, as you can listen and absorb knowledge whether you’re on the move, doing the dishes, or just relaxing at home. If you want to know more about big data, then here are some of the best podcasts you’ll want to be listening to right now (Headlines of all entries are linked to each mentioned podcast!)

1. Freakonomics

 You may well know about the book Freakonomics by Stephen Dubner. In it, he uncovered the world of data science for the average reader, and showed them just how it affected their everyday lives. In this podcast, he carries on the work he started in the book to help you understand the world of big data.

There are several episodes that you’ll want to make sure you listen to, such as The Health of Nations, which looks at how health is measured across the world. Everybody Gossips is another good episode, as it covers how our Google search histories expose our true selves to those who are evaluating that data.

2. Data Framed

This podcast is a must listen if you’re looking to learn more about big data. Trends are changing all the time in this field, so you want to make sure you’re on top of the game. “Each episode brings on an expert in their field, so you can learn from the best” says tech writer Adrian Bowman, from Boom Essays and OXEssays. “You’ll get a real insight into how they use data, and what that means for you.”

Recent episodes have covered things like Salesforce was created to be a mature data organization, and how to build a data science team from scratch. They’re all fascinating to listen to, so you’ll want to make sure that you tune in.

3. Data Skeptic

 With so many episodes in the archive, you can go back and listen to this show for days on end. Every episode covers a different concept in data science, so it’s really helpful to anyone that’s learning about it for the first time. Even if you’re an expert though, you’ll find some new perspectives in here.

You don’t have to start at the beginning to listen, though. Instead, you can catch up with the latest episodes that cover everything new in data. For example, they’ve recently released episodes on the user perceptions of ‘bad ads’ online, and political digital advertising analysis.

4. Data Crunch

This podcast is very much aimed at people who are already working with big data in some way. As such, it won’t be as accessible to newcomers to the field. However, if you are someone in the field then you’ll want to subscribe to this show.

You’ll find lots of episodes on how machine learning is changing industries across the board, as well as some showing where it hasn’t been the success that companies were looking for. You’ll see a lot about what works and what doesn’t here, so you can see what will make your business thrive in the future.

5. Not So Standard Deviations

 On the other hand, this is the podcast you’ll want to be listening to, if you’re new to data science and want to learn more. “The chemistry between the hosts makes it a very easy listen” says Dean Simmons, a big data blogger at State Of Writing and Paper Fellows. “That makes it a lot more accessible for those who are beginning to learn about the subject.”

You’ll get all the basics on things like social media algorithms, deprecated packages, app testing, and much more here. You’ll learn a lot and enjoy listening, too.

6. Making Data Simple

 Finally we have this podcast, which looks at bringing you the very latest news in big data, in a way that’s easy to understand. It’s another show that’s worth listening to if you’re already working in data, as it looks at the news from the viewpoint of those in industries where data is vital.

Host Al Martin talks to experts every episode, so you’ll be able to get the news from the people who know about it, and see how it will affect you.

All these shows can give you a lot of info about big data, so give them a listen and see which one is right for you.

Benjamin Aunkofer

6 Steps of Process Mining – Infographic

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Many Process Mining projects mainly revolve around the selection and introduction of the right Process Mining tools. Relying on the right tool is of course an important aspect in the Process Mining project. Depending on whether the process analysis project is a one-time affair or daily process monitoring, different tools are pre-selected. Whether, for example, a BI system has already been established and whether a sophisticated authorization concept is required for the process analyzes also play a role in the selection, as do many other factors.

Nevertheless, it should not be forgotten that process mining is not primarily a tool, but an analysis method, in which the first part is about the reconstruction of the processes from operational IT systems in a resulting process log (event log), the second step is about a (core) graph analysis to visualize the process flows with additional analysis/reporting elements. If this perspective on process mining is not lost sight of, companies can save a lot of costs because it allows them to concentrate on solution-oriented concepts.

However, completely independent of the tools, there is a very general procedure in this data-driven process analysis you should understand and which we would like to describe with the following infographic:

DATANOMIQ Process Mining - 6 Steps of Doing Process Mining Analysis

6 Steps of Process Mining – Infographic PDF Download.

Interested in introducing Process Mining to your organization? Do not hesitate to get in touch with us!

DATANOMIQ is the independent consulting and service partner for business intelligence, process mining and data science. We are opening up the diverse possibilities offered by big data and artificial intelligence in all areas of the value chain. We rely on the best minds and the most comprehensive method and technology portfolio for the use of data for business optimization.