AI and Automation in Cybersecurity: Future Skilling for Efficient Defense

In today’s interconnected digital landscape, cybersecurity has become a critical concern for individuals, organizations, and governments alike. The ever-evolving threat landscape poses significant challenges, requiring innovative approaches to defend against sophisticated attacks. One approach gaining traction is the integration of artificial intelligence (AI) and automation into cybersecurity practices. The combined power of AI and automation offers the potential for more efficient defense, enabling proactive threat detection, real-time monitoring, and automated incident response. However, realizing the full potential of these emerging technologies requires a workforce equipped with the necessary skills and knowledge. This underlines the significance of investing in future-oriented cybersecurity training.

It is imperative that cybersecurity professionals explore the potential advantages and challenges of AI and automation and the skill sets required to adapt and excel in this rapidly changing landscape.

Challenges in Cybersecurity

The complexity and scale of cyberattacks continue to surge, with threat actors employing advanced techniques to exploit vulnerabilities. Traditional manual approaches to threat detection struggle to keep pace with the speed and volume of attacks, necessitating more intelligent solutions.

Take, for example, the 2020 SolarWinds attack.¹ Hackers illicitly accessed SolarWinds, a prominent IT management software provider, to embed malicious code into software updates. The compromised updates reached numerous SolarWinds clients including government agencies and enterprises. The attackers operated covertly for months, showcasing the modern cyberthreat landscape’s escalating complexity. Manual threat detection methods faltered in identifying this attack, given its covert nature and the extensive data volume impacted by the attack. Consequently, this incident underscores the demand for a proactive and adaptable security strategy and a skilled cybersecurity workforce capable of countering such advanced threats.²

Ransomware attacks have also become a significant and pervasive challenge in the cybersecurity landscape. The scale and impact of ransomware attacks have escalated dramatically in recent years, causing severe disruptions and financial losses. Notable instances include the 2017 Equifax breach, which exposed sensitive personal information,³ and the 2021 Colonial Pipeline attack, which disrupted fuel supply chains.⁴ These attacks showcase the devastating consequences of failing to secure data and systems adequately. Innovative solutions such as advanced endpoint protection, behavior-based anomaly detection, and secure backup and recovery systems are crucial in mitigating the risk posed byran somware.⁵

Phishing campaigns also continue to be a persistent threat. They have become more sophisticated, with cybercriminals crafting convincing emails and messages, often impersonating trusted entities, to trick individuals into revealing sensitive information or clicking on malicious links. Well-known examples include the 2014 Sony Pictures breach and the Yahoo data breaches of 2013 and 2014.⁶ Traditional email filtering methods are often insufficient to stop these types of attacks. The adoption of cutting-edge technologies to bolster defenses is needed. Innovative email security solutions employ machine learning (ML) and AI to analyze email content and sender behavior, improve detection rates, and reduce false positives.⁷

To confront these challenges effectively, a proactive, adaptable, and continuously improving approach to cybersecurity is not only a requirement, it is imperative to safeguarding the digital world.

Potential Benefits of AI and Automation

To address the challenges of cybersecurity, AI and automation offer tangible advantages. AI algorithms can be used for multifaceted applications including intricate threat detection, anomaly identification, and predictive analysis. Notably, automation techniques such as orchestration and response automation can be used to lay the groundwork for streamlined incident response, reduction of critical response times, and application of ethical decision-making frameworks. Literature showcases a plethora of instances where AI and automation have been successfully harnessed, encompassing domains such as malware identification, network surveillance, analysis of user behavior patterns,⁸ augmented threat intelligence, real-time monitoring capabilities, expedited response durations, and the curtailment of human error. In addition, real-time AI-powered monitoring ensures continuous analysis of vast datasets, allowing for prompt detection of anomalies and potential breaches.

Consider a prominent financial institution that employs AI and automation in cybersecurity. The system identifies an unusual surge in transaction requests across multiple accounts. Instant alerts are sent to the cybersecurity team, triggering automated responses. As a result, affected accounts are secured, suspicious transactions are halted, and customers are promptly informed. The security team collaborates with law enforcement, traces the attack, and neutralizes the threat. This example shows how AI-driven automation minimizes financial losses, upholds customer trust, and expedites threat detection and mitigation. Furthermore, automation streamlines incident response, automates routine tasks, expedites remediation, and reduces response time.

Potential Drawbacks of AI

The integration of AI and automation offers immense promise, but it is essential to understand the potential downsides, address ethical concerns, and uphold responsible practices associated with these transformative technologies.⁹ Potential harm and challenges that organizations must be aware of include:

• False positives and negatives–False positives occur when AI systems incorrectly identify benign activities as threats, leading to unnecessary alerts and alert fatigue among cybersecurity professionals. False negatives, on the other hand, happen when AI fails to recognize true threats, leaving organizations vulnerable to attacks. For instance, in 2019, an AI-based antivirus system flagged a critical system file as malware. A necessary file was therefore removed, resulting in thousands of computers becoming inoperable and causing significant disruption for affected users.¹⁰
• Data privacy concerns–The use of AI in cybersecurity often involves processing sensitive data, which raises concerns about data privacy. Mishandling or exposure of data can result in regulatory violations, financial penalties, and damage to an organization’s reputation. DeepMind, a subsidiary of Google, faced scrutiny over a partnership with the UK’s National Health Service (NHS) where concerns were raised about patient data access and the use of AI for processing sensitive medical information.¹¹ Organizations must ensure that they handle data responsibly and comply with relevant data protection regulations.
• Sophisticated adversarial attacks– AI systems are susceptible to adversarial attacks, where malicious actors manipulate input data to deceive AI algorithms. Attackers use advanced techniques to craft inputs that AI systems misclassify, allowing malicious activities to go undetected. For instance, in autonomous driving, attackers might place strategically designed stickers on road signs to mislead a self-driving car’s object recognition system, potentially causing accidents. As another example, in 2018, researchers demonstrated how they could fool AI-powered facial recognition systems by using specially designed glasses and accessories. This raised concerns about the security of biometric authentication systems.¹²

If teams trust AI to handle all security tasks, it can result in a lack of human oversight and critical thinking, leading to the system potentially missing critical security issues.
• Limited context understanding–AI systems can struggle to grasp the broader context of events. They may flag legitimate activities as threats or fail to recognize suspicious behavior due to their limited contextual understanding. For example, in 2020, an AI system security camera in a retail store misidentified an employee restocking shelves as a shoplifter. The employee was detained, highlighting the potential risk of AI surveillance systems misunderstanding real-world situations.¹³
• Dependence on constant updates–AI in cybersecurity relies on continuous updates to remain effective against evolving threats. Failure to update AI models and threat databases can leave organizations vulnerable to new attack vectors. For example, the 2017 WannaCry ransomware attack affected organizations worldwide, including healthcare and financial institutions, because they had failed to update their systems with the necessary security patches. This underscores the importance of regular updates and patch management.¹⁴
• Lack of explainability–AI algorithms, particularly deep learning models, are often seen as black boxes because they are challenging to interpret. This lack of explainability can hinder organizations’ abilities to understand why AI systems make specific decisions, affecting transparency and trust. For instance, in 2019, an AI–-driven loan approval system used by a major bank was criticized for its lack of transparency. Applicants were denied loans without understanding why, leading to customer complaints and regulatory scrutiny.¹⁵
• Overreliance–Overreliance on AI can lead to complacency among cybersecurity professionals. If teams trust AI to handle all security tasks, it can result in a lack of human oversight and critical thinking, leading to the system potentially missing critical security issues. For example, in 2016, a large technology enterprise experienced a security breach when its AI-driven authentication system was tricked by a cybercriminal. The attacker initiated a phishing campaign by crafting deceptive emails that appeared legitimate, potentially mimicking official communications or urgent alerts. These emails, designed to manipulate human psychology, contained malicious links or attachments. As employees, trusting the apparent legitimacy of the communication, interacted with these phishing attempts, the cybercriminal successfully tricked them into revealing sensitive information, including login credentials. In this case, the organization placed excessive reliance on AI without incorporating supplementary measures. The AI authentication system failed to detect the anomaly in the login behavior, leading to unauthorized access to sensitive systems. This incident serves as a stark reminder that although AI can enhance security, it should be part of a broader, multilayered approach that combines technological advancements with traditional security methods, ensuring a more resilient defense against evolving cyberthreats.¹⁶

  In a highly concerning scenario, the use of fully autonomous weapons in military applications presents a significant risk. These weapons have the capability to make life-and-death decisions without direct human intervention, marking a departure from traditional command and control structures. The inherent danger lies in the lack of human oversight during the decision-making process. Delegating critical choices to AI systems poses a considerable risk that autonomous weapons might misinterpret situations because they lack the nuanced understanding, emotional intelligence, and contextual awareness that human decision makers possess. This raises the specter of unintended harm, as the autonomous systems may inadvertently cause collateral damage, harm civilians, or even lead to the escalation of conflicts. The potential for these weapons to act without a human in the loop raises ethical, moral, and strategic concerns, emphasizing the need for careful consideration and international agreements to govern their deployment and ensure responsible use in military contexts.

• Skills gap and human job displacement–The automation capabilities of AI may lead to a shift in the required skill sets for cybersecurity professionals. Some routine security tasks may become automated, displacing employees and potentially creating skill gaps in handling emerging threats. In recent years, as banks and financial institutions have implemented AI for automated fraud detection and customer support, some employees have been displaced or had to adapt to new roles. This shift has presented challenges in terms of retraining and maintaining a skilled cybersecurity workforce.

It is paramount to prioritize responsible implementation, transparency, and ethical decision-making frameworks to mitigate potential hazards. There is a pressing necessity for comprehensible and interpretable AI algorithms, but there is a scarcity of proficient professionals well-versed in both AI and cybersecurity domains, and a latent risk of adversarial attacks targeting AI systems.¹⁷

Understanding and addressing these potential drawbacks are essential steps in harnessing the full potential of AI and automation while mitigating the associated risk.

Future Skilling for AI-Based Cybersecurity

The dynamic nature of cybersecurity mandates a paradigm shift in professional skill sets. Alongside conventional expertise, future cybersecurity professionals need proficiency in AI and ML, adeptness in data analytics, advanced programming competencies, and an unwavering grasp of fundamental cybersecurity principles. For example, in case a cybersecurity analyst faces an AI-generated malware attack, they must understand AI algorithms and be able to anticipate and thwart AI-driven threats to counter the attack. In another example, data analytics professionals can leverage the power of AI and automation to uncover vulnerabilities in vast datasets. Through the use of ML algorithms, massive amounts of raw data can be processed and analyzed at an unprecedented speed and scale. AI algorithms assist in identifying patterns, anomalies, and potential security risk that would be virtually impossible for a human to discern manually. This enables the data analyst to turn raw data into actionable insights quickly and efficiently, helping their organization stay ahead of potential threats and vulnerabilities in its cybersecurity efforts. Mastery of automation tools for routine tasks frees attention to focus on innovative strategies against cybercriminals.

The integration of AI and automation components into cybersecurity education curricula and immersive training programs is crucial. Ongoing learning and continuous upskilling is essential to stay current in this evolving landscape. By incorporating ongoing learning into business practices, organizations will be well-equipped to skillfully navigate the constantly changing threat landscape and keep pace with rapid technological advancements.18

AI and Automation in Action

The potential of AI and automation to revolutionize cybersecurity practices is most evident when examining specific use cases. These scenarios showcase how AI-driven technologies can outperform traditional methods, leading to more effective defense strategies. The following examples also underscore the importance of a multifaceted skill set that combines traditional cybersecurity knowledge with AI, data analysis, and automation proficiencies to effectively safeguard digital ecosystems:

• Malware detection and analysis–AI-powered systems can swiftly analyze large datasets to detect subtle patterns indicating the presence of zero-day malware. Consider a network under the threat of an emerging zero-day malware varianta type of malware that exploits a software vulnerability that is unknown to the software vendor or the cybersecurity community. This type of threat is particularly significant because there are no known patches or defenses available to protect against it. Traditional signature-based detection methods might fail to recognize this novel threat, but AI-powered analysis can swiftly identify anomalous behavior. It does so by initially learning what normal behavior looks like on the network, establishing a baseline and then continuously monitoring for any deviations from this baseline. When it detects activity that falls outside the established norm, it raises an alert, enabling immediate containment and remediation. Detecting and mitigating cybersecurity threats such as malware demands an understanding of AI algorithms, proficiency in analyzing vast datasets, and the ability to establish baselines for normal network behavior.
• Behavioral anomaly detection–Automation can be used to continuously monitor network traffic and user behavior, such as user and entity behavior analytics (UEBA), to quickly identify deviations from established norms. For example, an AI system can detect when an employee accesses sensitive data outside of regular working hours. The system triggers an alert, enabling prompt investigation and preventing potential data breaches. Behavioral anomaly detection requires expertise in configuring automated monitoring systems, knowledge of network traffic analysis, and the capability to identify deviations from established user behavior norms.
• Phishing attack detection–AI can analyze email content and sender behaviors to identify potential phishing attacks. An automated system can detect subtle language cues, domain impersonations, and unusual sender patterns, all of which may indicate a potential phishing attack. This automated detection can significantly reduce the risk of employees falling victim to phishing scams. Phishing attack detection relies on recognizing subtle language cues and patterns and being able to configure automated systems to respond swiftly to potential threats.
• Vulnerability assessment–AI-powered automation can scan and analyze code to pinpoint vulnerabilities in software applications. Automated vulnerability scanners can analyze source code repositories for potential security flaws and suggest remediation actions, enabling developers to proactively enhance software security. For vulnerability assessment, professionals need competence in AI-powered tools, familiarity with software development and source code analysis, and the ability to proactively identify and address security flaws.
• Automated incident response–In the event of a security breach, AI-driven automation can swiftly isolate affected systems, halt malicious processes, and notify cybersecurity teams. This rapid response minimizes damage and prevents the spread of attacks, showcasing the advantages of AI in incident response. Essential skills for humans include system configuration and incident management expertise.
• Endpoint protection–AI can be used to protect endpoints (computers, devices, servers) from various threats, including malware and ransomware. AI algorithms analyze behavior patterns to identify potentially malicious activities on endpoints, allowing for proactive threat mitigation. Practitioners must acquire skills in AI algorithms and behavioral analysis for threat detection on computers, devices, and servers.
• Network traffic analysis–AI-based automation tools can analyze network traffic in real time to identify unusual patterns or anomalies, indicating potential security breaches or cyberattacks. This is especially crucial for large-scale networks where manual monitoring is impractical. To build expertise in this domain, practitioners should focus on acquiring skills in specific AI algorithms tailored for real-time analysis of network traffic. Moreover, behavioral analysis is a key competency that enhances the practitioner’s ability to discern irregularities indicative of security threats. Hands-on skills in configuring and managing security measures on computers, devices, and servers are indispensable. This includes practical knowledge in implementing and maintaining security measures such as firewalls, intrusion detection systems, and encryption protocols. As organizations invest in skilling initiatives, a holistic approach should be adopted, encompassing not only technical skills but also an awareness of the evolving threat landscape. Cybersecurity professionals should stay informed about emerging risk and technologies, fostering a proactive mindset that is essential for adapting security measures. By developing a well-rounded skill set that includes AI algorithms, behavioral analysis, and practical security management, practitioners can effectively contribute to the robust defense of networks against the ever-evolving challenges of cybersecurity.
• Security information and event management (SIEM)–SIEM solutions enhanced with AI and automation can process vast amounts of security data, correlate events, and provide real-time insights into potential threats. This assists security analysts in making faster and more informed decisions. Proficiency in AI algorithms is a fundamental skill for practitioners working with SIEM as it enables them to effectively leverage the AI capabilities integrated into SIEM systems. Understanding and implementing AI algorithms enhances the accuracy and efficiency of threat detection, ensuring that the SIEM system can effectively identify and respond to evolving cyberthreats. In addition, a strong foundation in behavioral analysis is crucial for practitioners utilizing SIEM solutions. SIEM systems process vast amounts of data, and the ability to analyze user and system behaviors becomes paramount in identifying anomalous activities that may indicate potential security threats. Practitioners should be adept at interpreting patterns, recognizing deviations, and discerning between normal and suspicious activities across computers, devices, and servers.
• Cloud security–AI and automation can be employed to monitor and secure cloud environments. They can automatically detect and respond to suspicious activities in cloud infrastructure, ensuring that data and applications hosted in the cloud are protected. First and foremost, proficiency in AI algorithms is paramount for practitioners engaged in cloud security. The integration of AI into cloud security solutions enables intelligent analysis of vast datasets, facilitating the identification of anomalous patterns and potential threats. This skill is essential for ensuring that cloud security measures can adapt to evolving cyberthreats and provide robust protection for cloud-hosted assets. In addition to AI expertise, a strong foundation in behavioral analysis is crucial for practitioners in the realm of cloud security. Cloud environments process diverse and dynamic data sets, and the ability to analyze user and system behaviors becomes instrumental in identifying potential security risk. Practitioners with skills in behavioral analysis can effectively discern normal from suspicious activities, ensuring a proactive approach to threat detection across computers, devices, and servers connected to cloud infrastructure.

Conclusion

The integration of AI and automation in cybersecurity represents a significant opportunity for efficient defense against evolving threats. The potential benefits of AI and automation in enhancing cybersecurity practices include improved threat intelligence, real-time monitoring, and automated incident response. However, responsible implementation and addressing ethical considerations are crucial to ensure trustworthy and transparent use of AI in cybersecurity.

Future skilling emerges as a key imperative in this rapidly evolving landscape. Cybersecurity professionals must acquire new skill sets, including proficiency in AI, ML, data analytics, and programming. Lifelong learning and continuous upskilling are essential to stay abreast of emerging technologies and evolving threats. Integrating AI and automation into cybersecurity curricula and training programs is necessary to prepare professionals for the changing cybersecurity landscape.

To fully realize the potential of these technologies, it is crucial to invest in future skilling initiatives that prioritize the development of relevant skills. By doing so, cybersecurity professionals can adapt to the evolving threat landscape, effectively defend against cyberattacks, and safeguard digital systems and information. The synergy between AI, automation, and skilled cybersecurity professionals will pave the way for a secure digital future.

Endnotes

¹ Oladimeji, S.; Kerner, S.; “SolarWinds Hack Explained: Everything You Need to Know,”TechTarget, 27 June 2023, https://www.techtarget.com/whatis/feature/SolarWinds-hack-explained-Everything-you.need-to-know
² N-able, “Common Cybersecurity Issues and Challenges,” 17 October 2019, https://www.n-able.com/blog/cyber-security-issues
³ Ng, A.; “How the Equifax Hack Happened, and What Still Needs to be Done,” CNET, 7 September 2018, https://www.cnet.com/news/privacy/equifaxs-hack-one-year-later-a-look.back-at-how-it-happened-and-whats-changed/
⁴ Easterly, J.; “The Attack on Colonial Pipeline: What We’ve Learned and What We’ve Done Over the PastTwo Years,” Cybersecurity and Infrastructure Security Agency, USA, 7 May 2023, https://www.cisa.gov/news-events/news/attack.colonial-pipeline-what-weve-learned-what-weve.done-over-past-two-years
⁵ Lynn, S.;Thorbecke, C.; “Why Ransomware Cyberattacks Are on the Rise,” ABC News, 4 June 2021,https://abcnews.go.com/Technology/ransomware-cyberattacks-rise/story?id=77832650
⁶ Global Initiative, “Ten Biggest Cyber Crimes and Data Breaches to Date,” 28 April 2017, https://globalinitiative.net/analysis/10-biggest.cyber-crimes-and-data-breaches-till-date/
⁷ Daswani, N.; Elbayadi, M.; “The Yahoo Breaches of 2013 and 2014,” Big Breaches, Apress, Berkeley, California, USA, 2021, https://link.springer.com/chapter/10.1007/978-1-4842-6655-7_7
⁸ Kaur, R.; Gabrijelčič.; D.; Klobučar,T.; “Artificial Intelligence for Cybersecurity: Literature Review and Future Research Directions,” Information Fusion, vol. 97, September 2023, https://doi.org/10.1016/j.inffus.2023.101804
⁹ Nazareno, L.; Schiff, D.; “The Impact of Automation and Artificial Intelligence on Worker Well-Being,” Technology in Society, vol. 67, November 2021, https://doi.org/10.1016/j.techsoc.2021.101679
¹⁰ Beaman, C.; Barkworth, A.; Akande,T.; et al.; “Ransomware: Recent Advances, Analysis, Challenges and Future Research Directions,” Computer Security, 24 September 2021, https://doi.org/10.1016%2Fj.cose.2021.102490
¹¹ BBC, “DeepMind Faces Legal Action Over NHS Data Use,” 1 October 2021, https://www.bbc.com/news/technology-58761324
¹² Tung, L.; “These Patterned Glasses Are All itTakes to Fool AI-Powered Facial Recognition,” ZDNET, 3 November 2016, https://www.zdnet.com/article/these-patterned-glasses-are-all-it-takes-to-fool-ai.powered-facial-recognition/
¹³ Vincent, J.; “This Japanese AI Security Camera Shows the Future of Surveillance Will Be Automated,”The Verge, 26 June 2018, https://www.theverge.com/2018/6/26/17479068/ai-guardman-security-camera-shoplifter-japan.automated-surveillance
¹⁴ Palmer, D.; “WannaCry Ransomware: Hospitals Were Warned to Patch System to Protect Against Cyber-AttackBut Didn’t,” ZDNET, 27 October 2017, https://www.zdnet.com/article/wannacry.ransomware-hospitals-were-warned-to-patch.system-to-protect-against-cyber-attack-but-didnt/
¹⁵ Hale, K.; “A.I. Bias Caused 80% of Black Mortgage Applicants to Be Denied,” Forbes, 2 September 2021, https://www.forbes.com/sites/korihale/2021/09/02/ai-bias-caused-80-of-black.mortgage-applicants-to-be-denied/
¹⁶ Markets and Markets, “Generative AI’s First Data Breach: OpenAITakes Corrective Action, Bug Patched,” 22 June 2023, https://www.marketsandmarkets.com/industry.news/Generative-AI-Breach-Openai-Takes-Action.Bug-Patched
¹⁷ Pietilä, A.-M.; Nurmi, S.-M.; Halkoaho, A.; et al.; “Qualitative Research: Ethical Considerations,” InThe Application of Content Analysis in Nursing Science Research, Springer, Switzerland, 2019, https://link.springer.com/chapter/10.1007/978-3-030-30199-6_6

SINGIRIKONDA MANIKANTA

Is a senior security analyst (penetration tester) with more than six years of experience in information security. Manikanta is a certified ethical hacker and security analyst proficient in application security and vulnerability assessments and has expertise in programming languages, cloud computing, and network security. Manikanta is committed to learning and staying updated in the field and has also contributed to research in areas such as cashless payment adoption and next-generation cryptography.