Of the many conversations surrounding artificial intelligence in business, its role in cybersecurity is among the most complex—and the most critical. Many businesses that deploy AI for cybersecurity experience notable advantages: enhanced threat detection, faster automated responses, and stronger safeguards for critical systems. However, the same capabilities driving these benefits also raise pressing concerns about privacy, ethics, and compliance.
The dilemma is clear: How can businesses deploy AI to protect against increasingly sophisticated attacks, while mitigating the threats posed by AI itself? The precise answer will vary based on the size of the organization, the regulations relevant to it, and the types of data it collects. Nonetheless, a thorough understanding of AI’s capabilities and limitations—coupled with a well-defined strategy for balancing intelligence gains with system governance—can enable most organizations to establish an appropriate role for AI within their cybersecurity frameworks.
Leveraging AI for threat detection and response
AI is capable of combing through vast amounts of cross-departmental data to quickly identify and flag anomalies, such as unusual account login patterns or suspicious email activity. It can also detect file behavior that indicates the presence of ransomware, viruses, and other types of malware. Because AI removes human error from the detection process, stakeholders often receive more accurate and timely warnings, which improves their ability to respond to incidents before they escalate.
Moreover, AI helps to support meaningful and high-value cybersecurity contributions from human stakeholders. Through advanced data analysis, it can help IT identify and prioritize security vulnerabilities and orchestrate effective incident responses. Meanwhile, automating routine processes (such as compliance monitoring or system scans for outdated software) frees up IT bandwidth, so employees can focus on tasks that require strategy, nuance, and advanced analytical skills (such as developing comprehensive incident response protocols or reverse engineering attacks on company software). When leveraged effectively, AI makes it possible for IT to invest their time in complex and impactful security initiatives without worrying that a critical task or process will slip through the cracks.
Accounting for the functional shortcomings of AI in cybersecurity
Though AI shows significant promise as a driver of improved cybersecurity, the need for human oversight persists. AI threat detectors can produce false positives—and more worryingly, false negatives—under various conditions. For example, novel attacks may go undetected because they differ from the patterns the AI was exposed to during training.
However, it isn't just the range of data used for training that impacts AI performance—it's the quality as well. When data hygiene is neglected, digital activity may be incorrectly labeled as either "benign" or "malicious," and critical information about new or emerging threats may be missing from the dataset. As a result, AI systems may deliver results that are skewed, bias, or simply incorrect. Further, if the data does not accurately represent the complexity of real-world operations within the company, legitimate variations or nuances in a given process may be flagged as suspicious. This can lead to unnecessary disruptions, wasted resources, and "alert fatigue" among stakeholders.
To help prevent AI performance issues that could undermine cybersecurity efforts, it is essential to routinely clean, label, and properly store relevant data (and if your AI models are developed externally, it's worth looking into how your vendor manages data hygiene). Additionally, retraining models as new data becomes available helps ensure an accurate representation of business operations—and of an ever-evolving threat landscape. Organizations that rely on a third-party for AI development and training should assess the vendor's data policies and any other measures in place to prevent bias and promote accuracy.
Understanding the vulnerabilities associated with AI models
AI is highly effective at monitoring user behavior and scanning files—but these functionalities can introduce significant data privacy concerns, especially in highly regulated industries like healthcare and legal. If proper access restrictions aren't in place throughout the organization, AI tools risk storing (or potentially exposing) private information about the company or its clients.
Part of the reason this is so alarming is because of the uncertainty surrounding the internal workings of AI solutions. This uncertainty—often called the "black box problem"—means that even if an AI system is instructed to "forget" the sensitive information it has scanned, there is no way to guarantee it has done so (this is one reason for the rising popularity of RAG).
Beyond data management concerns, the black box problem also makes it difficult to discern precisely how AI is arriving at its conclusions and recommendations. This can make it challenging for IT personnel to trust AI-driven insights and make confident decisions that serve cybersecurity objectives. Compounding the credibility issue, many AI systems remain susceptible to manipulation by malicious actors.
Because deep-learning models are sensitive to changes in input, cyber attackers can alter data to tamper with AI outputs. And if an attacker gains access to the system's training data, they may do more than compromise the integrity of the model; they could access and expose sensitive information, posing serious risks to data privacy, security, and compliance.
Why a balanced approach to AI is vital for cybersecurity
Despite the risks AI poses, it would be impractical to ignore its potential contributions to cybersecurity—especially given the rapid rise in cyberattacks businesses are facing (since the introduction of generative AI, phishing attacks alone have increased by a staggering 1,200%). However, it is crucial to implement guardrails that ensure AI is bolstering—rather than detracting from—these initiatives.
By regularly auditing AI systems, logging training activity and outputs, and performing routine penetration testing, IT teams can quickly identify vulnerabilities in AI-powered cybersecurity solutions and prevent minor issues from causing severe consequences. Robust data governance policies support these efforts by restricting AI's access to sensitive data and reducing the impact of any potential exposure.
These processes and restrictions are common to a "human-in-the-loop" approach, which invites live employees to engage with AI systems for tasks like validating model outputs, reviewing AI decisions, and approving AI-prompted actions. While such an approach may cost businesses some of the efficiency gains AI has promised, "human in the loop" remains significantly faster than human-led processes. Moreover, it can be vital to AI success in cybersecurity, empowering IT personnel to act as both a critical fail-safe and a strategic enabler of highly informed, contextual decision-making.
While the most tech-savy organizations may already be looking ahead to a future of autonomous AI cybersecurity tools, close monitoring and skilled employee intervention remain crucial amid current technological vulnerabilities. Combining the power and functionality of AI with the expertise and advanced problem-solving skills of live employees will be key to managing ongoing threats—without giving rise to new threats in the process.
