The Biggest Challenges For Cybersecurity and Artificial Intelligence

Cybersecurity and artificial intelligence (AI) play a critical part in our digital lives. AI presents itself in our homes, at work and everywhere we go giving attackers the ability to continuously develop sophisticated ways of taking advantage of security vulnerabilities. Yet, cybersecurity pros are beginning to find ways to use the technology to improve cyber defense capabilities which will help protect against attacks and mitigates the risk of data breaches.

Here’s how AI can help:

• Pattern recognition: AI can quickly identify patterns in data that humans would miss.
• Automated response: AI can automatically respond to threats as they are identified.
• Learning: AI can learn from past experiences and improve its cybersecurity capabilities.

 Of course, AI in cybersecurity comes with challenges. Despite the promising use of AI and machine learning as a proactive solution to the growing cybersecurity threats, it's hard to track and anticipate emerging cybersecurity threats in real-time.

Cybercrime on the rise: According to a Cyberwarfare report by Cybersecurity Ventures, cybercrime will cost businesses across the globe $10.5 trillion each year by 2025.

AI Needs Quality Data to Operate Optimally

It is expensive to get high-quality data for AI technology to process, identify patterns, and create safe solutions based on its analytical findings. As a result, most data used to develop existing AI cybersecurity solutions is from the public, insecure sources, or AI systems integrated with consumer and business technologies.

This opens defense contractors to added security risks resulting from misunderstood inputs or false positives from poor data signals. For instance, attackers can introduce malicious training data through backdoors to generate potentially dangerous cybersecurity outcomes from AI systems.

Also, data policies, regulations, and privacy laws create a barrier for big data and AI since consumers rarely give consent for data collection if they do not understand the complexity of the data systems.

Shortage of AI and Cybersecurity Skills

AI use in cybersecurity has increased the net volume of reported cybersecurity incidents. In turn, the adoption of AI to augment, not replace, human cybersecurity analysts has inspired a growing demand for AI-powered solutions for cybersecurity.

 This is well and dandy except for one thing: the human capacity to respond to these cases and rectify breach concerns is almost static. Why?

 The number of knowledgeable professionals with niche expertise in the AI and cybersecurity industry is not enough to capitalize on the advantages of AI technology for enhancing cybersecurity.

 Additionally, AI technology is advancing at speeds humans can't comprehend. The high cost and time it takes to train individuals to become certified experts and specialists in the industry might leave a skill gap.

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In fact, the global cybersecurity workforce can only successfully defend organizations' critical assets if it grows by 65%. This means AI for cybersecurity is still bound to face challenges and risks because of issues like rushed deployments, insufficient oversight, misconfigured systems, and improper risk assessments.

AI is Vulnerable to Attacks

AI is not invincible to cyberattacks; hackers do manipulate and access security networks through AI systems. For instance, a hacker can exploit an AI-enabled program to consider malicious software normal or safe.

In that case, measures like biometric authentication that are considered pragmatic security systems become liabilities. A hacker uses such a system to learn more about an organization's security pattern and then attack undetected.

An excellent example is the use of deep fake data to create voice matches or facial videos that simulate insiders in an organization to bypass humans and automated systems within security protocols.

Hackers also access public AI data to create more sophisticated attacks. Attackers use machine learning to determine why certain cyberattacks fail, meaning that attack solutions developed in response are both sophisticated and effective.

Increase in Social Engineering

Social engineering attacks leverage AI to influence and manipulate societal or individual behavior. While this is an ongoing threat, its growth is expected to expand to unprecedented levels in the future. Why?

Advancements in AI technology will continuously make it more challenging to differentiate between humans and AI on the internet. Therefore, there is a massive threat that humans will become more susceptible to AI-empowered attacks because social engineering will become the perfect gateway.

It Takes One to Know One

Organizations in the cybersecurity industry must be faster and more intelligent than cyber criminals if they are to strengthen network defenses using AI-empowered solutions. This requires a system that automates and continuously monitors and reports cyber incidents in real-time.

Further, the proactive AI and machine learning cybersecurity procedures must be robust and comprehensive to cover every infrastructure of an organization's network. Still, the successful implementation of AI for cybersecurity aimed at gathering future insights into possible cybersecurity attacks is an expensive and challenging process.

Also, an initial learning curve to avoid future hiccups requires time that defense contractors cannot afford. This is why more than half of the implementation projects fail the first time.

Possible Solutions

There is a need for continued investment in R&D to improve all AI-powered solutions for protecting systems against cyberattacks. Defense contractors can leverage AI to enhance security protection and automate processes that overwhelm security workers.

For instance, machine logic easily reduces the rate of false positives by filtering data before it is processed by AI systems. Also, AI and machine learning simplify complex protection and response processes.

This leaves the workforce to focus their attention on more productive decision-making responsibilities. Altogether, data needs to be interchanged to create a space where AI understands threat patterns wholly and accurately.

This creates a need for data transparency between governments, businesses, and customers. In sum, everyone is responsible for ensuring relevant information regarding cyber-attacks and their detection is shared.

Additionally, an investment must be made to train skilled AI and machine learning workers. It is imperative to understand, create, and optimize security, and this is achieved by a workforce that can skillfully engineer and monitor core R&D.

This means that government and private defense contractors should invest in training their workforce and position them to graduate with proper cybersecurity specialization certificates. Overall, the implementation of AI and machine learning for cybersecurity should be categorized into prevention, detection, investigation, remediation, and threat intelligence.

Cybersecurity Needs AI, AI Needs Us

AI can reduce cyber-attacks at a massive scale, making it invaluable in the cybersecurity industry. Nonetheless, AI professionals are also needed to make it easier to understand and verify the results of security protection as provided by AI technologies. Connect with an AI professional today and see where you vulnerabilities lie.