AI audits

Artificial intelligence (AI) has emerged as a potentially game-changing tool for security professionals. However, given its meteoric rise in popularity, it can be challenging to sort the benefits from the hype. Organizations that hope to use AI effectively must know where its advantages and downsides truly lie.

Security audits are an excellent use case to analyze. These processes are critical to maintaining high security standards amid evolving threats. Their time-consuming, data-heavy and repetitive nature theoretically makes them ideal for AI. What do the results say as more organizations automate these processes? Are AI-driven auditing approaches more effective than manual alternatives?

Benefits of AI Security Audits

AI security audits have been around for a few years, and results are promising along many lines. Here are some of the leading benefits of AI-driven audits that organizations have experienced.

Comprehensive Coverage

AI’s attention to detail is one of its greatest strengths for security audits. Repetitive analytical work is prone to error when done manually, as it’s easy for people to become distracted or tired, leading them to miss details. By contrast, AI doesn’t have the same weaknesses, allowing it to spot vulnerabilities human analysts may overlook.

A 2023 study found that AI threat detection identified exploits 300% more accurately than conventional methods. Much of this improvement stems from its ability to go beyond traditional binary decision-making. AI can account for varying conditions, helping it distinguish between threats and benign behavior on a situational basis.

An analysis of 72 other studies confirmed that AI consistently outperforms conventional threat detection methods. Deep learning and ensemble models, in particular, showcased accuracies above 99% across multiple applications.

Reduced Timelines

AI security audits are also much faster than manual alternatives. An AI model can thoroughly analyze multi-page documents in minutes while retaining a consistent accuracy level. Even the most experienced auditors can’t match that speed because AI can interpret information faster than humans can read it.

Conventional auditing is a time-consuming process. Maintaining security compliance takes businesses 4,300 hours a year on average. While not all that time goes to auditing, these reviews account for a significant portion of that time consumption. Automating audit processes through AI gives security teams free hours to spend on mission-critical tasks.

Real-Time Alerts

This accuracy and timeliness lets AI move beyond the conventional security audit and perform real-time threat detection. Security teams can’t reasonably perform continuous monitoring manually, especially considering the 3.4 million worker talent gap in cybersecurity. AI can automate this monitoring to enable around-the-clock security despite workforce strains.

AI can detect when a new vulnerability arises or a threat breaks through the defenses in real time. It can contain a breach and alert security teams for faster, more effective responses. It can also flag issues so network administrators can patch them sooner.

These real-time alerts shouldn’t replace conventional annual audits but supplement them. Adopting a more agile approach to security optimization ensures organizations’ protections remain reliable and relevant despite fast-evolving threats.

Where AI Falls Short

Security teams must view these admittedly impressive benefits in context. AI-driven auditing also introduces unique shortcomings that may temper expectations around this technology.

Most notably, AI detection models have a false positive problem. They tend to overclassify situations as risky, which leads to comprehensive review and protection but can also produce alert fatigue. More than half of security workers feel overwhelmed by alert volumes, so these false positives may lead to increased burnout and take time away from real issues.

Bold claims about AI’s speed and accuracy may also lead teams to over-rely on these tools. AI may be more accurate than conventional methods, but that doesn’t mean it’s perfect. If teams take all AI results at face value given its high accuracy, they may become complacent and miss crucial errors. Consequently, even though the audit results may be more reliable, the responses may be less effective.

Where to Take the AI Audit From Here

In light of AI’s benefits and risks, security audits should approach this technology carefully. AI’s accuracy, speed and real-time functionality are too advantageous to ignore its potential. Real-world case studies highlight the importance of these benefits, with AI saving companies $1.76 million on average in the event of a data breach.

However, AI’s risks are similarly too prominent to overlook. Organizations that want to make the most of this technology must consider these downsides and how to account for them.

Training AI on large amounts of data relevant to the specific organization is crucial, as it boosts accuracy and lowers the risk of false positives. Security teams must also adopt new, AI-centric workflows. That includes having a formal but streamlined process for responding to alerts and flagging false positives, verifying AI insights before acting on them, and tweaking AI models to become more reliable over time.

AI Security Audits Are Imperfect but Important

As AI gets more real-world audit experience, its downsides will shrink. For now, though, it remains an impressive but imperfect tool.

AI audits are faster and more accurate than manual alternatives but are only effective when teams account for their downsides. Organizations that can address these issues can safely proceed with AI-driven audit optimization.