Challenges of Adopting Artificial Intelligence (AI) in Cybersecurity

Adopting Artificial Intelligence (AI) in Cybersecurity
Adopting Artificial Intelligence (AI) in Cybersecurity

Cyber threats issues are on an exponential rise and there are many challenges facing the cybersecurity teams for a new operational reality.

The pandemic accelerated digital transformation and has changed the dynamics of digital enablement for businesses, and it has increased the scope of cybersecurity issues by multifold.

With more interconnected and interdependent kinds of systems in existence, the scope for data breach, cyber security concerns and cyber attack instances have expanded significantly.

This has led to businesses taking cybersecurity issues more seriously than ever before.

Increasing the range of threat vectors, the range of vulnerabilities is high, and the cybersecurity management teams are forced to consider robust and sophisticated measures for increasing cybersecurity vigilance.

To counter the emerging cybersecurity threats, the dynamics were focused on the AI (Artificial Intelligence) based artificial intelligence security system.

Globally the industry experts are pro to the adoption of artificial intelligence solutions as a security tool.

Some large-scale businesses are already adapting artificial intelligence security systems for managing cybersecurity.

AI Application impacts security opeations

As shown in a report by IBM on artificial intelligence security systems for cybersecurity, the above set of improvements is evident from the execution.

Artificial intelligence systems, when used effectively, are seen to have the right kind of predictive analysis in place, which can help in improving the quality of cybersecurity managed in a real-time environment.

The other key reason for the preference for AI security systems is the issue of human resource engagement or managing multiple screening systems for cybersecurity.

Effectively used, artificial intelligence-empowered cybersecurity systems can deliver the desired results for the process of detecting any intrusions, predicting the vulnerabilities in the systems, etc.

Aiming at the benefits of automation, the role of artificial intelligence security systems will be evolving more and will be in great demand.

Some of the key areas of cybersecurity practice wherein the role of artificial intelligence systems is becoming significant are

  • Detection of new threats
  • Battling the bots
  • Breach risk analysis
  • Enhanced endpoint protection
  • Improved dynamics of statistics and impact scale analysis

Use of the artificial intelligence solutions is resulting in dynamics of preventing the cybersecurity challenges faster than the conventional screening models employed for cybersecurity.

However, there are some challenges integral to managing the application of artificial intelligence solutions for business, and how the cybersecurity teams must address the challenges for improved cyber protection for the business environment.

Challenges of Adopting Artificial intelligence for Cybersecurity 

artificial intelligence

1. Resource requirements 

Globally the adoption of machine learning models and artificial intelligence solutions for various operations and functions of a business is evolving.

However, considering the need for the business to equip the server infrastructure, and expertise of resources for managing the AI security system, there are huge costs involved for the businesses.

While the large-scale tech corporations might be in a position to absorb such costs for artificial intelligence system development and deployment, for small-scale businesses in need of cybersecurity solutions, such options of artificial intelligence security systems might be out of their affordability in the current scenario.

2. Economies of Scale 

Like the on-premises infrastructure costs for administration of IT systems, the cost of managing the artificial intelligence security systems is also high.

In the current market scenario, few companies have worked on the integration of AI security systems into their cybersecurity applications services.

Unless the service providers offer the artificial intelligence security system as a SaaS (Software as a Service) model for the businesses, not many businesses shall have the budgets and bandwidth to embrace AI security systems for cybersecurity.

Related: Learn more about SaaS Cloud Computing.

3. Quality of Data used in Training Sets

Technically, the effectiveness of an artificial intelligence security system depends on the quality of training provided to the classifier machine learning algorithm.

In the case of cybersecurity conditions, the need for the systems is about developing robust and synthetic systems of data useful for training the system or dependence on real-time data from the cybersecurity instances for training the system.

Also, basis the classifiers used and algorithm models, the predictive outcome accuracies for cybersecurity must be analyzed from various quarters.

Unless the tech giants involve in the process and have distinctive sets of mathematical and artificial intelligence modeling for the systems, it could be very challenging for getting a more accurate range of cybersecurity systems.

4. Evolving Dynamics of Risk 

New age threats are fast changing, and the quantum of threats emerging over time is phenomenal.

While the cybersecurity teams are identifying the preventive solutions for some new threats, multiple other threats are emerging the in the public domain.

While the scope of artificial intelligence solutions application seems a potential solution for the process, still the magnitude of its implementation has many complexities.

For a simple scenario, if the artificial intelligence security system is trained over non-reinforced learning models with specific datasets, the security system can be resourceful only to detect and label the threats as per its learning.

Thus, there is a need for reinforced learning models, for improved accuracy in cybersecurity solutions.

In the absence of a good volume of data and events, artificial intelligence systems might fail to render accurate results and thus, the issues of cybersecurity monitoring shall depend on unreliable conditions.

To have such reinforced learning and continuous learning practice from the AI security system, there is a need for a regular flow of data on various kinds of vectors, the impact factors, metrics resourceful for analysis of vectors, and its risk levels for cybersecurity.

With more than half of the cybersecurity breaches not being reported in the public domain, the challenges for the businesses are high in terms of collating the data, compiling them into a systematic process for analysis, training the algorithms, and shaping up a potential cybersecurity system as the artificial intelligence security system.

However, in the current scenario, there is a certain set of artificial intelligence security systems available in the public domain from reputed cybersecurity service providers, and they can be used for the protection of business systems.

To know more about one such cybersecurity solution from Acronis Cyber-Protect, Engage with the Exabytes Malaysia team.

Acronis Cyber Protect

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About Acronis: All-in-One Cyber Protection for Businesses

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