Artificial intelligence is transforming many industries but few as dramatically as cybersecurity. It’s becoming increasingly clear that AI is the future of security as cybercrime has skyrocketed and skills gaps widen, but some challenges remain. One that’s seen increasing attention lately is the demand for explainability in AI.
Concerns around AI explainability have grown as AI tools, and their shortcomings have experienced more time in the spotlight. Does it matter as much in cybersecurity as other applications? Here’s a closer look.
What Is Explainability in AI?
To know how explainability impacts cybersecurity, you must first understand why it matters in any context. Explainability is the biggest barrier to AI adoption in many industries for mainly one reason — trust.
Many AI models today are black boxes, meaning you can’t see how they arrive at their decisions. BY CONTRAST, explainable AI (XAI) provides complete transparency into how the model processes and interprets data. When you use an XAI model, you can see its output and the string of reasoning that led it to those conclusions, establishing more trust in this decision-making.
To put it in a cybersecurity context, think of an automated network monitoring system. Imagine this model flags a login attempt as a potential breach. A conventional black box model would state that it believes the activity is suspicious but may not say why. XAI allows you to investigate further to see what specific actions made the AI categorize the incident as a breach, speeding up response time and potentially reducing costs.
Why Is Explainability Important for Cybersecurity?
The appeal of XAI is obvious in some use cases. Human resources departments must be able to explain AI decisions to ensure they’re free of bias, for example. However, some may argue that how a model arrives at security decisions doesn’t matter as long as it’s accurate. Here are a few reasons why that’s not necessarily the case.
1. Improving AI Accuracy
The most important reason for explainability in cybersecurity AI is that it boosts model accuracy. AI offers fast responses to potential threats, but security professionals must be able to trust it for these responses to be helpful. Not seeing why a model classifies incidents a certain way hinders that trust.
XAI improves security AI’s accuracy by reducing the risk of false positives. Security teams could see precisely why a model flagged something as a threat. If it was wrong, they can see why and adjust it as necessary to prevent similar errors.
Studies have shown that security XAI can achieve more than 95% accuracy while making the reasons behind misclassification more apparent. This lets you create a more reliable classification system, ensuring your security alerts are as accurate as possible.
2. More Informed Decision-Making
Explainability offers more insight, which is crucial in determining the next steps in cybersecurity. The best way to address a threat varies widely depending on myriad case-specific factors. You can learn more about why an AI model classified a threat a certain way, getting crucial context.
A black box AI may not offer much more than classification. XAI, by contrast, enables root cause analysis by letting you look into its decision-making process, revealing the ins and outs of the threat and how it manifested. You can then address it more effectively.
Just 6% of incident responses in the U.S. take less than two weeks. Considering how long these timelines can be, it’s best to learn as much as possible as soon as you can to minimize the damage. Context from XAI’s root cause analysis enables that.
3. Ongoing Improvements
Explainable AI is also important in cybersecurity because it enables ongoing improvements. Cybersecurity is dynamic. Criminals are always seeking new ways to get around defenses, so security trends must adapt in response. That can be difficult if you are unsure how your security AI detects threats.
Simply adapting to known threats isn’t enough, either. Roughly 40% of all zero-day exploits in the past decade happened in 2021. Attacks targeting unknown vulnerabilities are becoming increasingly common, so you must be able to find and address weaknesses in your system before cybercriminals do.
Explainability lets you do precisely that. Because you can see how XAI arrives at its decisions, you can find gaps or issues that may cause mistakes and address them to bolster your security. Similarly, you can look at trends in what led to various actions to identify new threats you should account for.
4. Regulatory Compliance
As cybersecurity regulations grow, the importance of explainability in security AI will grow alongside them. Privacy laws like the GDPR or HIPAA have extensive transparency requirements. Black box AI quickly becomes a legal liability if your organization falls under this jurisdiction.
Security AI likely has access to user data to identify suspicious activity. That means you must be able to prove how the model uses that information to stay compliant with privacy regulations. XAI offers that transparency, but black box AI doesn’t.
Currently, regulations like these only apply to some industries and locations, but that will likely change soon. The U.S. may lack federal data laws, but at least nine states have enacted their own comprehensive privacy legislation. Several more have at least introduced data protection bills. XAI is invaluable in light of these growing regulations.
5. Building Trust
If nothing else, cybersecurity AI should be explainable to build trust. Many companies struggle to gain consumer trust, and many people doubt AI’s trustworthiness. XAI helps assure your clients that your security AI is safe and ethical because you can pinpoint exactly how it arrives at its decisions.
The need for trust goes beyond consumers. Security teams must get buy-in from management and company stakeholders to deploy AI. Explainability lets them demonstrate how and why their AI solutions are effective, ethical, and safe, boosting their chances of approval.
Gaining approval helps deploy AI projects faster and increase their budgets. As a result, security professionals can capitalize on this technology to a greater extent than they could without explainability.
Challenges With XAI in Cybersecurity
Explainability is crucial for cybersecurity AI and will only become more so over time. However, building and deploying XAI carries some unique challenges. Organizations must recognize these to enable effective XAI rollouts.
Costs are one of explainable AI’s most significant obstacles. Supervised learning can be expensive in some situations because of its labeled data requirements. These expenses can limit some companies’ ability to justify security AI projects.
Similarly, some machine learning (ML) methods simply do not translate well to explanations that make sense to humans. Reinforcement learning is a rising ML method, with over 22% of enterprises adopting AI beginning to use it. Because reinforcement learning typically takes place over a long stretch of time, with the model free to make many interrelated decisions, it can be hard to gather every decision the model has made and translate it into an output humans can understand.
Finally, XAI models can be computationally intense. Not every business has the hardware necessary to support these more complex solutions, and scaling up may carry additional cost concerns. This complexity also makes building and training these models harder.
Steps to Use XAI in Security Effectively
Security teams should approach XAI carefully, considering these challenges and the importance of explainability in cybersecurity AI. One solution is to use a second AI model to explain the first. Tools like ChatGPT can explain code in human language, offering a way to tell users why a model is making certain choices.
This approach is helpful if security teams use AI tools that are slower than a transparent model from the beginning. These alternatives require more resources and development time but will produce better results. Many companies now offer off-the-shelf XAI tools to streamline development. Using adversarial networks to understand AI’s training process can also help.
In either case, security teams must work closely with AI experts to ensure they understand their models. Development should be a cross-department, more collaborative process to ensure everyone who needs to can understand AI decisions. Businesses must make AI literacy training a priority for this shift to happen.
Cybersecurity AI Must Be Explainable
Explainable AI offers transparency, improved accuracy, and the potential for ongoing improvements, all crucial for cybersecurity. Explainability will become more critical as regulatory pressure and trust in AI become more significant issues.
XAI may heighten development challenges, but the benefits are worth it. Security teams that start working with AI experts to build explainable models from the ground up can unlock AI’s full potential.
Featured Image Credit: Photo by Ivan Samkov; Pexels; Thank you!