Explainable AI Models for Transparent Decision-Making in Cybersecurity

Sometimes, the strengths of artificial intelligence (AI) are also its weaknesses. Its inability to remain interpretable despite confidently outputting insights could significantly weaken your organization’s security, hindering its ability to defend against cyber threats. Can developing explainable AI models circumvent these risks?

AI’s Role in Cybersecurity Decision-Making

AI’s prevalence in cybersecurity decision-making increases as adoption barriers drop and its benefits become apparent. Its automation capabilities and rapid processing speed may be enticing if you spend too much of your workday on tedious tasks.

You likely already recognize how beneficial AI’s rapid processing and analysis could be. Cybersecurity teams receive tens of thousands of security notifications daily — most of which are false positives — leading to chronic alert fatigue. Automation technology would prevent those errors and accelerate review, lightening the workload.

AI would be a welcome addition to most cybersecurity departments because it can reduce false positives by more than 70%, minimizing the time spent manually reviewing errors. Its role in decision-making stems from its performance, accuracy, and speed, as it can analyze faster than leading industry professionals.

You may have noticed AI’s worth increasing exponentially as it transitioned from being a fringe technology to entering the mainstream. In the cybersecurity industry alone, experts estimate its market value will surpass $134 billion by 2030, up from $24.3 billion in 2023. Such a significant jump in under a decade has driven adoption.

Why AI Model Transparency Is an Issue

In cybersecurity, transparency and security go hand in hand — which is why your leaders have likely taken steps to implement those factors into their decision-making process. For example, they may have transitioned to the cloud since it heightens visibility and accessibility between departments. In fact, 72% of survey respondents agree it is somewhat or much more secure than what they can deliver on-premises themselves.

An approach like that is part of cultivating a culture of trust and accountability, which is crucial when a single mistake or misguided choice can result in cyberattacks and data breaches. Much like the cloud, AI is a tool that is often fundamental to the decision-making process, so it must be interpretable.

Unfortunately, AI systems get more complex as they grow more capable — and they advance daily. However, even as their logic becomes harder to follow, your trust in them likely doesn’t diminish since their competency and effectiveness increase. You’d do well to remember these models have no reasoning, critical thinking, or context awareness.

Regulatory agencies’ scrutiny increases the longer AI remains unchecked. Moreover, your board’s watchful gaze intensifies the moment of deployment. An explainable model should be your priority because transparency will soon become key for compliance and generating revenue with algorithmic insights. More importantly, its interpretability in the decision-making role determines how secure your employer is.

If you’re like many tech leaders, you assume there must be a tradeoff between explainability and accuracy, suggesting your ability to comprehend an AI’s decision-making process somehow invalidates it. In reality, the two factors are unrelated. According to one study of almost 100 representative data sets, an explainable model maintains its accuracy 70% of the time on average.

Explainability Techniques You Should Consider

A decision tree is among the most simplistic approaches to explainable AI, but it works. It is a standalone machine learning (ML) model that places values along the logical process depending on their importance. The more important they are, the higher they are. Since you can branch out as many times as you need, it remains effective with complex algorithms. Also, since you explicitly define each stage of the decision-making process, abnormalities are clear.

The decision tree surrogate model is ideal for predictive AI. You train it on the “black box” output your original model produces to see the flow of the decision-making process. Since it’s model-agnostic, it doesn’t need the original dataset or knowledge of the other algorithm’s inner workings to function properly.

Shapley Additive exPlanations is a method for explaining ML model output. It assigns an importance value to each feature to measure each one’s effect on the final decision-making outcome. It’s based on cooperative game theory — the Shapley value considers all possible combinations to determine a player’s expected marginal offering, determining whether they contributed more or less than the others.

An alternative is Accumulated Local Effects, a method for determining how individual inputs affect the output while isolated from the others. For example, if you created a model to predict the weather using factors like humidity, temperature, air pressure, and wind speed, you could see how much temperature affects the prediction without considering the other variables.

One emerging technique is Transparency Relying Upon Statistical Theory (TRUST) Explainable AI. It is a high-performance, model-agnostic algorithm that provides explanations for random samples with a 98% success rate on average. Its creators claim it is superior to Local Interpretable Model-agnostic Explanations, another common explainability technique, in speed, performance, and comprehensibility.

How to Improve AI Decision-Making Transparency

You can leverage the human-in-the-loop method, ensure adequate data preprocessing, adopt an explainability technique, and develop an AI governance framework to improve algorithmic decision-making transparency in cybersecurity. Whatever approach you choose, remember that prioritizing transparency is critical for maintaining security.

Originally posted on OpenDataScience.com

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