AI and Cybersecurity: The Dual Role of Automation in Threat Mitigation and Attack Facilitation

Written by Anand Naik, CEO and Co-Founder at Sequretek

Artificial intelligence (AI) is no longer a novel concept; it is a keystone of our daily lives and industries. From autonomous vehicles to virtual assistants, AI shapes the way we live and work. In cybersecurity, this evolution is particularly transformative, presenting both unprecedented opportunities and challenges. The real question is not whether AI is good or bad but how we harness its power – whether to bolster defenses or aid attackers.

According to a Statista report, the AI cybersecurity market, valued at USD 24.3 billion in 2023, is expected to double by 2026 and reach nearly USD 134 billion by 2030. This reflects a growing reliance on machine learning (ML) and natural language processing (NLP) to detect, protect against, and respond to cyberthreats on a larger scale. As cyberattacks escalate against critical infrastructure and high-profile sectors such as technology and government, the urgency for AI-driven cybersecurity solutions has never been greater.

Cybersecurity professionals have long been in a race against evolving threats. AI-powered automation has revolutionized this field by enabling real-time data analysis, threat detection, autonomous response, and generative AI-based contextual search and analytics. Yet, the same technology that fortifies our defenses also equips cybercriminals with advanced tools. AI-driven attacks—ranging from sophisticated phishing schemes to brute-force attacks to botnet-based DDoS assaults—demonstrate how attackers leverage AI to amplify their reach and effectiveness. Moreover, AI contributes to the development of more adaptable malware, creating threats that are increasingly difficult to detect. This constant interplay between defensive and offensive AI highlights a crucial truth: AI’s efficacy is inextricably linked to the strategy guiding its use.

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Despite its potential, AI is not without risks that could compromise its effectiveness in cybersecurity. A key issue is algorithmic bias. An AI system can only perform as well as the data it learns from. Incomplete or biased datasets can lead to inadequate threat detection or false positives. For instance, an AI model may overlook certain malware types due to insufficient representation in its training data. These biases are not merely technical flaws—they translate into real vulnerabilities. To mitigate such risks, organizations must rigorously audit and refine their AI models, enhancing datasets and continually testing to ensure accuracy and fairness. Deep learning AI with unsupervised learning models is another approach used by technology developers to eliminate the learning bias.

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The reliance on data is another double-edged sword. While extensive data improves AI performance, it raises privacy and security concerns. If not properly secured, sensitive information becomes an attractive target for cybercriminals. Integrating AI into cybersecurity demands stringent data governance practices to protect personal and proprietary information. Adopting data minimization, encryption, and secure data-sharing is essential to safeguard against breaches without compromising AI efficiency. As regulatory frameworks evolve, such as Europe's GDPR, organizations must ensure their AI systems adhere to domestic and international standards.

AI’s automation capabilities do not replace the need for human judgment. Alert fatigue is one of the biggest problems in threat detection and response. Complex scenarios and nuanced decision-making still require human expertise, particularly as cybercriminals devise novel tactics to circumvent AI defenses. Over-reliance on automation can create dangerous gaps, exposing systems to sophisticated social engineering or new threat vectors not anticipated by AI training. Thus, transparency in AI operations is critical. "Explainable AI" is gaining traction to make AI’s decisions and actions understandable to users and auditors alike. This clarity helps reduce false positives—such as incorrectly flagging legitimate activities as threats—and ensures smoother, more reliable operations. Human oversight remains crucial for detecting subtle indicators of malicious intent that automated systems might miss.

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As AI becomes increasingly integral to cybersecurity, ethical considerations are coming to the fore. Discussions about an AI Bill of Rights in the U.S. and global efforts to standardize AI ethics highlight the need for robust oversight in sensitive areas like cybersecurity. These regulations will shape AI’s role in protecting digital environments. Addressing bias reduction, data privacy, and transparency is not just a best practice—it is an ethical obligation. In an interconnected world where data security is paramount, organizations that prioritize ethical AI development will lead to building resilient defenses.

The dual nature of AI—its capacity to protect and attack—presents an ongoing challenge for the cybersecurity community. With careful planning, ethical oversight, and continuous innovation, AI can be leveraged for defense to secure an accelerating digital era and ensure a more resilient future.

About the Author

Anand Naik, Co-Founder & CEO, has worked in the corporate world for over 25 years with companies such as Symantec where he was the MD for South Asia, and previously with IBM and Sun Microsystems in technology roles.

Anand is a subject-matter expert in cybersecurity. He has worked with several global giants in helping them define their IT security strategy, architecture, and execution models. He is among the top thought leaders in cybersecurity and has participated in various policy programs with the Government of India and other industry bodies. He is responsible for product vision and operations at Sequretek.