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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

Veteran-owned businesses are gathering to talk AI and Cybersecurity at Military Appreciation Night Join us at Regency Furniture Stadium, Aug 19th (link to event) 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

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