AI-Driven Growth Hacking Tactics: Pros and Cons of Relying on Machine Intelligence for Cybersecurity

AI-Driven Growth Hacking Tactics: Pros and Cons of Relying on Machine Intelligence for Cybersecurity

Introduction

Although sophisticated hackers and AI-fueled cyberattacks tend to hijack the headlines, one thing is clear: The biggest cybersecurity threat is human error, accounting for over 80% of incidents. This is despite the exponential increase in organizational cyber training over the past decade, and heightened awareness and risk mitigation across businesses and industries. Could AI come to the rescue? That is, might artificial intelligence be the tool that helps businesses keep human negligence in check? In this article, we will explore the pros and cons of relying on machine intelligence to de-risk human behavior.

The Impact of Cybercrime

The impact of cybercrime is expected to reach an enormous figure this year, surpassing the GDP of all countries in the world except the U.S. and China. Furthermore, the figure is estimated to increase to nearly $24 trillion in the next four years. Although sophisticated hackers and AI-fueled cyberattacks tend to hijack the headlines, one thing is clear: The biggest threat is human error, accounting for over 80% of incidents. This, despite the exponential increase in organizational cyber training over the past decade, and heightened awareness and risk mitigation across businesses and industries. Could AI come to the rescue? That is, might artificial intelligence be the tool that helps businesses keep human negligence in check? And if so, what are the pros and cons of relying on machine intelligence to de-risk human behavior?

The Growing Interest in AI-Cybersecurity Tools

Unsurprisingly, there is currently a great deal of interest in AI-driven cybersecurity, with predictions suggesting that the market for AI-cybersecurity tools will grow from just $4 billion in 2017 to nearly $35 billion net worth by 2025. These tools typically include the use of machine learning, deep learning, and natural language processing to reduce malicious activities and detect cyber-anomalies, fraud, or intrusions. Most of these tools focus on exposing pattern changes in data, such as enterprise cloud, platform, and data warehouse assets, with a level of sensitivity and granularity that typically escapes human observers. For example, supervised machine-learning algorithms can classify malignant email attacks with high accuracy, spotting “look-alike” features based on human classification or encoding, while deep learning recognition of network intrusion has achieved impressive results. As for natural language processing, it has shown high levels of reliability and accuracy in detecting phishing activity and malware through analyzing email domains and messages where human intuition generally fails.

The Limitations of AI in Cybersecurity

As scholars have noted, though, relying solely on AI to protect businesses from cyberattacks is a double-edged sword. Most notably, injecting just 8% of “poisonous” or erroneous training data can decrease AI’s accuracy by a whopping 75%, which is not dissimilar to how users corrupt conversational user interfaces or models by injecting preferences or language into the training data. In fact, AI’s reliability and accuracy to prevent past attacks is often a weak predictor of future attacks. Furthermore, there is a lack of trust in AI, particularly when people are under time pressure, and this often leads to a diffusion of responsibility in humans, which in turn increases their careless and reckless behavior. Instead of improving the much-needed collaboration between human and machine intelligence, the unintended consequence of relying solely on AI for cybersecurity is that it ends up diluting human responsibility. This highlights the importance of continuing efforts to educate, alert, train, and manage human behavior in organizations.

Trust in AI and Human Expertise

To be sure, businesses must educate themselves about the constantly changing landscape of cybersecurity risks, which will only grow in complexity and uncertainty due to the growing adoption and penetration of AI, both on the attacking and defensive end. While it may never be possible to completely extinguish risks or eliminate threats, the most important aspect of trust is not whether we trust AI or humans, but whether we trust one business, brand, or platform over another. This calls not for a binary choice between relying on human or artificial intelligence to keep businesses safe from attacks, but for a culture that manages to leverage both technological innovations and human expertise in the hopes of being less vulnerable than others. Ultimately, this is a matter of leadership: having not just the right AI tools, but also the right safety profile at the top of the organization, and particularly on the board. Organizations led by conscientious, risk-aware, and ethical leaders are significantly more likely to provide a safety culture and climate to their employees, in which risks will still be possible, but less probable. These companies can be expected to leverage AI to keep their organizations safe, but it is their ability to also educate workers and improve human behavior that will make them less vulnerable to attacks and negligence.

Conclusion

As cybersecurity threats continue to evolve, it is crucial for businesses to weigh the pros and cons of relying on AI-driven growth hacking tactics. While AI can be a powerful tool in detecting and preventing cyberattacks, it is not a magic bullet. Human error remains a significant threat, and organizations must invest in training and educating their employees to mitigate this risk. Additionally, trust must be placed in both AI and human expertise, as it is the combination of the two that will ultimately lead to stronger cybersecurity defenses. By understanding the limitations and potential of AI, businesses can develop a comprehensive approach to cybersecurity that leverages both technology and human intelligence.