Introduction
It is ubiquitously accepted that AI is our most efficient counterpart. We’re all using it to some capacity, trusting and relying on its abilities not to replace, but to enhance our everyday lives. But just like human intelligence, understanding must be subjected to questioning time and again — artificial intelligence needs to be challenged similarly.
AI models rely on inputs, perform data processing and normalization, use feature extraction, learn to assign weights and biases during training, to arrive at an output that they consider the most appropriate. This decision-making process is often complex and unclear, raising a critical question: why does AI arrive at a certain output the way it does?
Understanding why an AI model produces a specific outcome is a key challenge. This is exactly the problem Explainable AI (XAI) addresses.
**What is XAI: Explainable AI or XAI refers to methods used to build AI models that incorporate human-understandable decision-making. XAI is regarded as the foundation of responsible AI. It is often used interchangeably with terms “interpretability,” “trustworthy,” and “reliability.”
Opening the black box: Why AI decisions must be challenged
AI does not understand outcomes the way humans do. It figures patterns, not intent, or context. In our 2026 cyber predictions blog, we stated the arrival of an era where AI dependence isn’t just for assistance; we’re giving it the power to decide and act. AI systems will increasingly make autonomous security decisions in the future.
However, here’s the caveat – lack of oversight can become detrimental, leading to bias, misclassifications, privacy concerns, and ethical failures. Therefore, Explainable AI (XAI) becomes non-negotiable in security/AI deployments, informing:
- The data AI is trained on (to prevent poisoning or manipulation),
- Its decision logic (to prevent bias or adversarial exploitation),
- The output validation process (so analysts can see why the model acted the way it did).
Black-box logic, transparency gaps, compliance risks, and accountability concerns around AI would lead to slowed, immature adoption if not managed in a timely manner. Addressing this, the security leaders are now shifting the question from “Where are we using AI?” to “What decisions are we allowing AI to make — and can we explain them?”
Explainability in practice: A real-world signal
This shift is already evident in real-world cybersecurity deployments. XAI is now increasingly seen as a development prerequisite, not an add-on. In security operations, it shows up as an essential connection to risk. Instead of analysts seeing just a score, they see the context behind that score, they see explainable context, traceable detection logic, decision rationale, and contributing features – turning AI from a black-box risk into a controllable security capability.
To shed light on its essential use cases in cybersecurity, the use of XAI in tracking identity fraud is discussed in a recently published AWS blog. The blog talks about how anti-fraud solutions (as here in the case of the betting industry, as mentioned), moving beyond rule-based threat escalations to more advanced and contextual controls to spot real abnormal user behavior is required.
Through proactive and faster detection of identity fraud and other digital threats, including malware, payment fraud, social engineering attacks, and bad bots, Group-IB Fraud Protection (FP) delivers more than just a risk score and includes XAI to provide the most complete anti-fraud solution in the market. It provides continuous behavioral analysis, enriched with high-quality explainable AI, enabling transparent and human-understandable explanations for its actions and decisions.
In high-risk digital sectors where AI-driven fraud decisions directly impact users, revenue, and regulatory exposure, explainability enables analysts to interpret and act on detections with confidence. This example reflects a broader industry trend: AI-powered security systems must not only detect threats, but also justify their decisions.
Building machine-human trust with XAI
As AI becomes embedded across cybersecurity workflows, explainability emerges as the connective tissue between detection, decision-making, and trust. Rather than serving a single function, XAI supports multiple security domains, from threat detection and SOC operations to fraud prevention and compliance, by enabling analysts to understand not just what the system decided, but why.
The table below shows how Explainable AI is a foundational layer across cybersecurity, connecting detection, fraud prevention, security operations, and governance by making AI decisions transparent and reliable.
| Security Domain | XAI Capability Used | What Explainability Does in Practice | Operational Impact |
| Detection & Response | Feature Attribution | Explains why an activity was flagged as malicious by showing which features (signals, behaviors, indicators) contributed to the detection. | Improves threat validation, reduces guesswork, and enables faster, evidence-based response decisions. |
| Fraud & Abuse Prevention | Explainable Modeling + Feature Attribution | Shows which behavioral signals (e.g., transaction patterns, session behavior) drove fraud or abuse decisions. | Enables accurate fraud detection, reduces false positives, and supports confident blocking or step-up actions. |
| Security Operations (SOC) | Behavioral Modeling + Feature Attribution | Reveals how behavioral deviations and contextual signals influence alerts and risk scoring. | Accelerates SOC triage, reduces alert fatigue, and improves analyst decision support. |
| Governance & Trust | Feature Attribution | Provides transparency into AI-driven decisions, including which factors influenced outcomes. | Supports auditability, compliance, accountability, and regulatory justification of AI decisions. |
| Authentication & Risk-Based Access | Behavioral Modeling + Explainable Modeling | Explains why step-up authentication or access restrictions were triggered based on behavior and context. | Enables proportional, risk-based authentication while reducing false rejections and user friction. |
| Investigations & Incident Analysis | Model Introspection | Allows teams to inspect how the model arrived at a decision, beyond just the final score. | Improves investigation quality, supports forensic analysis, and enables defensible incident reporting. |
| Risk Scoring | Model Introspection | Translates binary decisions into explainable risk scoring with contributing factors. | Enables nuanced decisions instead of hard blocks, improving control and adaptability. |
| Human-in-the-Loop Analysis | Explainable Outputs Across Layers | Makes AI decisions understandable to analysts and investigators at decision time. | Ensures human oversight, reduces blind automation, and strengthens trust in AI-driven security systems. |
Where XAI in cybersecurity makes a practical difference
Privacy:
XAI helps teams understand, verify, and control how AI models handle and process information. It enables teams to identify privacy issues, limits unnecessary data exposure (such as hidden data leakage between features or correlations that may violate privacy safeguards), and ensures that data is used only for its intended purpose, such as flagging cyber threats and vulnerabilities. In doing so, XAI helps demonstrate accountability in AI-driven security systems.
Reducing false positives:
AI may falsely flag normal activity as suspicious (due to tight thresholds, evolving attacker techniques, or environmental changes), leading to thousands of alerts with limited explanation and increased effort for evaluation. With XAI, teams can see which signals pushed an alert over the threshold, evaluate which features and data attributes influenced the model’s output, identify sources of noise, and tune detections to make triage more effective. In fraud prevention, XAI has been shown to significantly reduce false positives when explainable techniques are applied. Group-IB Fraud Protection integrates XAI into its detection logic to help security teams understand and act on alerts with greater confidence.
Operationally: Instead of the usual risk scoring, XAI helps answer critical questions – Why did this alert fire? Which signals mattered most? Was this a strong or borderline decision? This helps deprioritize alerts and reduce false positives.
Cyber investigations: Cybersecurity teams cannot solely rely on an alert or risk score to understand the scope of an attack. They need evidence. Explainable AI makes decision logic traceable. For example, in fraud systems, XAI techniques (such as SHAP or LIME) help security teams understand why a user session or transaction was flagged, reducing investigation time and focusing resources. This enables teams to determine whether a threat is legitimate, supports evidence-based incident reporting, and improves response effectiveness.
Operationally: During an investigation, XAI provides traceable decision logic indicating which behaviors deviated from baseline, how activity evolved over time, and whether this matches known attack patterns.
So, instead of assuming, teams can comment, “This session was flagged because behavior diverged from the user’s historical profile while originating from a high-risk environment.” This further helps reconstruct the attack path, link alerts to concrete evidence, and decide whether this is fraud, misuse, or benign behavior.
XAI enables regulatory and compliance readiness: Regulators, stakeholders, and security teams expect AI-driven decisions to be explainable when they impact customers or critical systems. XAI provides clear justification for actions taken, helps identify inappropriate handling of sensitive attributes, and supports consistent dispute resolution. By enabling auditability, accountability, and fairness, XAI reduces regulatory risk and strengthens compliance posture — including defensible explanations for account restrictions or step-up authentication.
Operationally: When regulators, auditors, or internal risk teams ask: why was this transaction blocked? Why was this account restricted? Was this decision fair and proportionate? XAI provides the rationale and evidence of proportionality that leads to effective audits, lower risk, and fewer unknown situations.
Authentication:
XAI supports authentication by providing risk-based, transparent, and interpretable explanations for decisions made by AI systems. This is particularly valuable in modern authentication workflows that rely on complex data or behavioral patterns that may be difficult for humans to interpret. For example, AI systems can analyze patterns in a user’s typing or mouse movement to assess identity. By applying XAI, organizations can better understand why step-up authentication is triggered, reduce false rejections, and improve trust in authentication decisions. Group-IB weaves XAI into its authentication approach through BioConfirm to help businesses make authentication both more transparent and more secure.
Operationally: In modern authentication (behavioral biometrics, continuous auth), XAI explains – Why was step-up authentication triggered? Why was a user blocked or challenged? Why was the friction increased for this session? This ensures step-ups are risk-based and also that only illegitimate users and actions are blocked.
Defense against adversarial AI: Organizations are keen on integrating AI into their security workflows, and adversaries know it. As a result, attacks target AI models directly instead of systems – through testing thresholds, manipulating input features, and exploiting black-box logic to escape detection.
Therefore, resistance to adversarial assaults becomes an essential component of artificial intelligence solutions. Explainable AI gives a proactive edge by exposing how models reason, which features drive outcomes, and where models may be susceptible to manipulation.
This helps prevent model drifts, identify adversarial manipulations to create more resilient models, fine-tune machine learning decisions, and support regulatory compliance.
Where does explainability live in the cybersecurity stack?
Explainability isn’t like a turnkey feature that can be attached to a solution. Instead, it is embedded into multiple layers of the security architecture to be effective.
- Development-level explainability ensures transparency across training data, model structuring, and real-world performance.
Model-level explainability helps teams understand how features influence outcomes, even in complex black-box models. - Data-level explainability enables the detection of data poisoning, manipulation, or unintended use of sensitive attributes.
- Decision-level explainability answers why a specific alert, transaction, or session was flagged.
- Operational explainability ensures analysts can act on AI outputs through faster triage, informed response, and effective audits.
When explainability is present across these layers, AI-driven security systems become more transparent, accountable, and adaptable.
Operationalizing XAI in Fraud Protection, and more
Explanations aren’t just inactionable/opaque scoring; they provide insight into why the model believes something is anomalous, which helps security analysts take informed actions. For strong use cases of XAI, read more about how Group-IB Fraud Protection integrates machine learning with explainability, acting as an operational lever for industries. Powered by XAI, real-time threat intelligence and advanced behavioural analytics, Group-IB Fraud Protection protects across web platforms, mobile apps, and APIs.
XAI isn’t just used to detect anomalies; it helps build multi-attribute behavioral profiles and interpretable signals that enable security teams to stay informed and act faster, with greater confidence. To know more about XAI and embedding it in your security stack, talk to experts now.
