January 2025 University of Latvia, Faculty of Computing, Raiņa bulvāris 18, Rīga
I. Introduction: The AI Conundrum in Cybersecurity’s New Epoch
The dawn of 2025 reveals a cyber threat topography that has escalated dramatically in sophistication and scale. Adversaries are increasingly adept at weaponising Artificial Intelligence (AI), deploying it to automate and refine their malicious endeavours. This includes the generation of polymorphic malware capable of evading traditional signature-based detection, the orchestration of highly convincing phishing and vishing campaigns, often augmented by deepfake technologies, and the execution of Advanced Persistent Threats (APTs) with unprecedented efficiency and stealth. Reports from early 2025 indicate a significant surge in AI-driven attacks, with malicious actors leveraging AI for creating advanced tools and launching highly targeted operations. The augmentation of spear-phishing, deepfakes, and APT campaigns through AI further underscores this dangerous trend. This evolving landscape necessitates a paradigm shift in defensive strategies, moving beyond reactive measures to more intelligent and adaptive approaches.
In this new epoch, AI presents a significant paradox: it is both an indispensable weapon in the cyber defender’s arsenal and a potent tool for malicious actors. The capacity of AI to analyse vast datasets, identify subtle anomalies indicative of compromise, and automate responses is crucial for contending with the current wave of advanced threats. Defensive AI tools are becoming increasingly powerful. However, this technological advancement is mirrored on the offensive side, creating a continuous arms race where both attackers and defenders leverage AI’s evolving capabilities. This escalating dynamic implies that maintaining a defensive edge requires not merely more powerful AI, but fundamentally smarter and, crucially, more transparent AI systems. Without transparency, the ability of defensive AI to adapt rapidly and to be trusted by human operators in this high-velocity environment is severely constrained.
A significant impediment to the effective utilisation of advanced AI in cybersecurity is the “black box” phenomenon. Many sophisticated AI models, despite their high accuracy in threat detection or prediction, operate with internal decision-making processes that are opaque to human understanding. This lack of transparency hinders the ability of human analysts to verify AI-generated alerts, debug system errors, identify potential biases, and ultimately, to trust the outputs of these systems. Such opacity can lead to critical delays in incident response, misinterpretation of complex threats, and an inability to satisfy increasingly stringent regulatory demands for accountability in automated decision-making. This “black box” issue is transitioning from a primarily academic concern into a substantial operational bottleneck. The absence of trust, born from a lack of understanding, directly impedes the speed and efficacy of cyber response, as hesitation or the need for extensive manual verification of opaque AI decisions can consume critical time during security incidents. For instance, studies have indicated that security teams augmented with explainable systems can respond to threats significantly faster.
It is in this challenging context that Explainable AI (XAI) emerges as a critical and transformative field. XAI is dedicated to the development of AI systems whose operations, outputs, and decision-making rationales can be rendered comprehensible to human users. XAI is not merely an additional feature; it represents a fundamental paradigm shift essential for the responsible, ethical, and effective deployment of AI in high-stakes domains such as cybersecurity. The drive towards XAI is further propelled by a strategic evolution in cybersecurity itself, moving towards more proactive and predictive postures. Such postures demand a deeper comprehension of subtle threat indicators and system vulnerabilities, insights that XAI is uniquely positioned to provide by illuminating the reasoning behind AI-driven predictions and alerts.
This paper, presented from the research perspective of the University of Latvia’s Faculty of Computing , aims to explore the pivotal role of Explainable AI in shaping the cybersecurity landscape of 2025. It will delve into the foundational principles of XAI, examine its diverse applications in bolstering cyber defences, critically assess the challenges and ethical considerations associated with its deployment, and project future trajectories for this vital field. This exploration underscores the University of Latvia’s commitment to advancing research in trustworthy, resilient, and human-centric AI systems. The central argument posits that XAI is an indispensable component for constructing robust, understandable, and ultimately more effective cyber defences in an increasingly complex digital world.
II. Demystifying Explainable AI: Foundations for Trustworthy Cybersecurity
To fully appreciate the transformative potential of XAI in cybersecurity, it is essential to understand its core tenets: transparency, interpretability, and explainability. While often used interchangeably, these concepts possess distinct meanings. Transparency refers to the degree to which the underlying mechanics of an AI model—its algorithms, data, and processes—are knowable. Interpretability concerns the ability to map inputs to outputs in a way that is comprehensible to a human; it is about understanding what a model does given certain inputs. Explainability, arguably the most ambitious goal, involves providing human-understandable reasons for why a model makes a specific decision or prediction in a particular context. True XAI strives for genuine human understanding, moving beyond superficial insights to offer clear, causal accounts of AI behaviour. This distinction is crucial, as a model might be interpretable (e.g., a simple decision tree) yet not offer satisfactory explanations for complex scenarios, or transparent in its code but too complex for its overall logic to be easily grasped.
In the high-stakes domain of cybersecurity, the imperative for XAI extends far beyond achieving high accuracy metrics. While accuracy is undoubtedly important, it is insufficient when AI-driven decisions can have profound consequences, such as incorrectly blocking legitimate network traffic, thereby disrupting critical business operations, or, conversely, failing to detect and stop a sophisticated attack, leading to catastrophic data breaches or system failures. XAI becomes crucial for several reasons: it allows human analysts to validate AI decisions, ensuring they are sound and contextually appropriate; it facilitates the debugging of models by revealing why they might be making errors; it helps in identifying and mitigating biases that may be present in the training data or model logic, thereby promoting fairness; and, critically, it fosters the trust necessary for effective human-AI collaboration within Security Operations Centres (SOCs) and other cybersecurity functions. Indeed, a preference for models with high interpretability, even at the cost of some accuracy, has been noted among cybersecurity professionals, underscoring the operational value of understanding.
The field of XAI encompasses a growing array of methodologies, each with specific strengths and applications relevant to cybersecurity. Among the most prominent are:
- LIME (Local Interpretable Model-agnostic Explanations): This technique explains individual predictions of any black-box model by approximating its behaviour locally with a simpler, interpretable model (e.g., a linear model or decision tree). In cybersecurity, LIME can be invaluable for understanding why a specific network alert was triggered or why a particular file was flagged as malware, providing localised insights into the model’s decision for that instance.
- SHAP (SHapley Additive exPlanations): Drawing from cooperative game theory, SHAP assigns an importance value (Shapley value) to each input feature, quantifying its contribution to a particular prediction. SHAP values offer a more consistent and theoretically grounded measure of feature importance compared to some other methods, providing both local and global explanations. For instance, SHAP can reveal which network traffic parameters or email characteristics most strongly influenced an AI’s decision to classify an event as anomalous or malicious.
- Rule-based and Tree-based Models: Unlike post-hoc techniques that explain black-box models, certain models like decision trees, random forests (to some extent), and explicit rule-based systems are inherently interpretable or “white-box.” Their decision logic is directly accessible. These can be used as primary predictive models or as explanatory approximations for more complex systems.
- Counterfactual Explanations: These explanations describe the smallest change to an input instance that would alter the model’s output. For example, a counterfactual explanation might show which specific attributes of an email, if changed, would cause a phishing detection model to classify it as benign instead of malicious. This helps in understanding a model’s decision boundaries and sensitivities.
By early 2025, these XAI techniques are demonstrably transitioning from purely theoretical constructs into practical tools. They are being actively integrated and rigorously evaluated within operational cybersecurity frameworks, particularly in areas like Intrusion Detection Systems (IDS) and digital forensics. This progression signifies a maturation of the XAI field, where its real-world utility in addressing the “black box” problem is becoming increasingly evident.
XAI effectively serves as a cognitive translation layer, enabling human cybersecurity experts to bridge the gap between their own domain knowledge, intuition, and contextual understanding, and the complex, often non-intuitive patterns identified by AI systems. AI excels at processing voluminous data and discerning patterns that might elude human perception. Conversely, human experts possess nuanced domain expertise and an understanding of broader context that AI models may lack. Opaque AI systems present their findings without facilitating this crucial reconciliation, potentially leading to misinterpretations by analysts or missed opportunities for synergistic decision-making. XAI, by elucidating how an AI model arrived at a conclusion, empowers human experts to compare the AI’s reasoning with their own knowledge base. This “translation” can validate AI findings, surface novel insights for human analysts, or help humans identify subtle flaws or biases in the AI’s logic, thereby fostering more effective and reliable human-AI collaboration.
However, the application of XAI is not a monolithic endeavour. The effectiveness of any given XAI technique is not absolute but is highly contingent upon the specific cybersecurity task at hand, the nature of the underlying AI model being explained, and, critically, the informational needs and cognitive characteristics of the human analyst. For instance, real-time threat detection within an IDS demands XAI methods that are computationally efficient and can provide rapid explanations , whereas post-incident digital forensic investigations might prioritise depth and completeness of explanation over speed. Similarly, explaining a deep neural network may require different techniques than explaining a gradient boosting machine. This context-specificity means there is no universal “best” XAI method; rather, a tailored approach to XAI implementation is necessary, requiring cybersecurity professionals to develop a nuanced understanding of the various XAI approaches and their respective trade-offs, such as the balance between computational overhead and explanatory power.
To provide a clearer understanding of these methodologies in practical contexts, Table 1 offers a comparative overview.
Table 1: Comparative Overview of XAI Techniques in Cybersecurity Contexts
| XAI Technique | Core Principle/Mechanism | Primary Cybersecurity Application | Key Benefit for Analysts | Notable Limitation |
|---|---|---|---|---|
| LIME (Local Interpretable Model-agnostic Explanations) | Approximates black-box model locally with an interpretable one for individual predictions. | IDS alert explanation, specific malware instance analysis, user behaviour anomaly rationale. | Provides intuitive, instance-specific reasons for a model’s decision. | Local scope (may not generalise well), sensitive to perturbation parameters. |
| SHAP (SHapley Additive exPlanations) | Uses game theory (Shapley values) to assign contribution scores to each feature for a prediction. | Feature importance in threat detection, malware family characterisation, risk factor identification. | Offers consistent and theoretically grounded feature attributions, local and global insights. | Computationally intensive for complex models and large datasets. |
| Rule-based Models (e.g., Decision Trees, RIPPER) | Inherently transparent models whose decision logic is expressed as a set of explicit rules. | Network traffic filtering, policy compliance verification, simple anomaly detection. | Easily understandable decision paths, direct insight into model logic. | May struggle with highly complex, non-linear relationships; can become unwieldy. |
| Counterfactual Explanations | Identifies minimal changes to an input that would alter the model’s output. | Understanding model decision boundaries, “what-if” scenario analysis for security policies. | Helps explore model sensitivity and robustness, aids in debugging. | Can be computationally expensive to find optimal counterfactuals, may not always be intuitive. |
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III. XAI in Action: Enhancing Cyber Defences in 2025
The practical application of Explainable AI is rapidly transforming various facets of cybersecurity, moving beyond theoretical potential to deliver tangible improvements in defensive capabilities. By early 2025, XAI is demonstrably enhancing the efficacy and trustworthiness of critical security systems and processes.
One of the most significant impacts of XAI is seen in the realm of Intrusion Detection Systems (IDS) and proactive threat intelligence. Traditional AI-powered IDS, while proficient at identifying anomalies, often leave analysts questioning the rationale behind specific alerts. XAI techniques, such as LIME and SHAP, are being integrated into IDS frameworks to provide clear insights into why certain network traffic patterns or behaviours are flagged as malicious or suspicious. This allows security analysts to validate alerts with greater confidence, significantly reducing the burden of false positives and enabling a more focused response to genuine threats. Furthermore, by understanding the specific indicators that contribute to an alert (e.g., unusual port activity, specific payload characteristics, anomalous data exfiltration patterns), analysts can better comprehend novel attack vectors and refine detection rules. This deeper understanding also enriches proactive threat intelligence; XAI can help explain the features and patterns that AI models identify as precursors to emerging threats or campaigns, enabling organisations to anticipate and mitigate risks more effectively.
XAI is also proving invaluable in augmenting malware analysis, digital forensics, and incident response. When analysing new malware samples, AI models can quickly classify them or identify malicious functionalities. XAI can then highlight the specific code sections, API call sequences, or behavioural characteristics that the AI model deemed indicative of maliciousness. This significantly accelerates the reverse engineering process for malware analysts. In digital forensics, the sheer volume of data can be overwhelming. AI can assist in sifting through this data to identify potential evidence, and XAI can then provide explanations for why certain data points (e.g., log entries, file fragments, network connections) were flagged as relevant to an intrusion. This explainability is crucial for reconstructing accurate attack timelines, understanding attacker methodologies, and ensuring that forensic findings are defensible. Consequently, incident response becomes faster, more targeted, and more effective, as teams can quickly grasp the nature and scope of an attack based on AI-driven, yet human-understandable, insights.
The contribution of XAI is particularly critical in ensuring robust security for Critical National Infrastructure (CNI), such as energy grids, transportation systems, financial networks, and healthcare facilities. The consequences of a successful cyberattack on CNI can be devastating, impacting public safety, economic stability, and national security. AI systems are increasingly deployed to protect these vital assets, but their opacity can be a major concern for operators and regulators. XAI provides the necessary transparency and interpretability to ensure that these AI-driven protection mechanisms are reliable, trustworthy, and auditable. This aligns directly with national cybersecurity strategies, such as Latvia’s, which emphasise the resilience of critical activities and infrastructure. Broader European Union initiatives also focus on enhancing the security and resilience of critical infrastructure, including new regulations for network and information systems and the protection of assets like submarine communication cables. For nations like Latvia, which are deeply integrated into regional energy and digital infrastructures and prioritise national resilience, the adoption of trustworthy and explainable AI for CNI protection is not merely a technical upgrade but a strategic imperative. Demonstrating the use of XAI in CNI signals a commitment to robust, verifiable security, which can enhance national resilience and build crucial confidence among international partners in shared security ecosystems.
Furthermore, XAI plays a vital role in facilitating regulatory compliance and auditability in AI-driven security. With the proliferation of data protection regulations like GDPR and emerging AI-specific legislation such as the EU AI Act, organisations are under increasing pressure to demonstrate that their AI systems are fair, transparent, and accountable. XAI provides the mechanisms to meet these demands. By offering explanations for how AI security systems make decisions (e.g., why a user’s access was flagged as anomalous, or how a data loss prevention system identified sensitive information), organisations can conduct more effective internal audits and provide clear justifications to external regulators. This capability is essential for demonstrating due diligence and maintaining trust with customers, partners, and regulatory bodies.
The integration of XAI into Security Operations Centres (SOCs) is fostering a more symbiotic relationship between human analysts and AI tools. XAI is not merely about making AI outputs understandable; it is about creating a collaborative environment where AI augments human capabilities, and human expertise, in turn, refines and improves AI models. When analysts understand why an AI system generates a particular alert, they can provide more targeted and meaningful feedback. For example, they can identify false positives caused by unique contextual factors that the AI might have missed, or confirm true positives with greater certainty based on the AI’s highlighted evidence. This feedback loop allows for the continuous learning and improvement of the AI models, leading to a more adaptive and resilient overall security posture. This human-machine symbiosis elevates the role of the analyst from a passive consumer of alerts to an active partner in a co-evolving security ecosystem.
By 2025, the utility of XAI is clearly expanding beyond its initial applications in threat detection. Its principles are proving valuable across a much wider spectrum of cybersecurity functions. This includes proactive risk assessment, where XAI can help explain the factors contributing to identified vulnerabilities or predicted attack likelihoods ; detailed forensic investigation, as discussed earlier ; and comprehensive compliance reporting. This broadening scope indicates that XAI is maturing from a niche solution for specific problems into a versatile and foundational technology capable of enhancing multiple facets of holistic security management.
IV. Navigating the Frontiers: Challenges and Ethical Imperatives of XAI
Despite the significant advancements and growing applications of Explainable AI in cybersecurity, its widespread and effective deployment is not without considerable challenges and profound ethical considerations. Navigating these frontiers requires a clear understanding of both the technical hurdles and the complex moral responsibilities involved.
Technical and Practical Hurdles are inherent in the current state of XAI development. A primary concern is the computational demand of many XAI techniques. Post-hoc explanation methods, particularly sophisticated ones like SHAP and some implementations of LIME, can be computationally intensive, requiring significant processing power and time to generate explanations. This overhead can limit their applicability in real-time, high-throughput cybersecurity scenarios, such as explaining every alert from an IDS monitoring a high-speed network.
Another persistent challenge is the interpretability-performance balancing act. There often exists a trade-off: highly complex AI models, such as deep neural networks, may achieve superior predictive accuracy but are inherently more opaque and difficult to explain comprehensively. Conversely, simpler models, like decision trees, are more inherently interpretable but might sacrifice some degree of performance in complex tasks. Finding the optimal balance, or developing techniques that can explain complex models without oversimplifying their behaviour, remains an active area of research. This very trade-off, however, acts as a significant driver for innovation, pushing researchers to develop novel AI architectures and XAI techniques that can minimize this gap, striving for systems that are both powerful and understandable.
Furthermore, issues of scalability and generalisability persist. Ensuring that XAI methods can scale effectively to the massive datasets and increasingly intricate models used in modern cybersecurity is crucial. Additionally, while local explanation methods like LIME provide valuable insights into individual predictions, ensuring that these local explanations can offer generalisable understanding or reliably reflect the global behaviour of the model is not always straightforward.
Beyond these technical issues lie Ethical Quandaries that demand careful consideration. The first among these is addressing algorithmic bias. AI models learn from data, and if that data reflects existing societal biases, the models will inevitably perpetuate, or even amplify, these biases. In a cybersecurity context, biased AI could lead to unfair targeting of certain user groups, disproportionate scrutiny of specific types of network traffic based on irrelevant demographic correlates, or the misidentification of legitimate software used by particular communities as malicious. While XAI can be a powerful tool for uncovering such biases by revealing the features influencing model decisions, the process of identifying, measuring, and mitigating these biases is itself complex and value-laden.
Closely related is the imperative of ensuring fairness and non-discrimination. Security systems augmented by XAI must be designed and audited to prevent discriminatory outcomes. This means going beyond simply detecting bias to actively promoting equitable treatment and avoiding the creation of systems that disadvantage or unfairly penalise individuals or groups.
Upholding privacy presents another ethical tightrope. XAI aims for transparency into model workings, but the explanations generated—if not carefully designed and controlled—could inadvertently reveal sensitive information about individuals whose data was used to train the model, or expose confidential details about an organisation’s security posture or system vulnerabilities. For instance, an explanation for an insider threat alert might reveal patterns of an employee’s behaviour that, while relevant to the alert, are also deeply personal. Balancing the need for explainability with the obligation to protect privacy is a critical design challenge.
The question of defining accountability also looms large. When an AI system, even one augmented by XAI, makes an erroneous decision that leads to harm (e.g., a significant financial loss due to a false positive, or a security breach due to a false negative), determining who is responsible—the AI developers, the organisation deploying the system, the human analyst who acted on the AI’s advice, or even the AI system itself in some abstract sense—is a complex legal and ethical puzzle. XAI can aid in tracing the decision-making pathway, which is a prerequisite for accountability, but it does not automatically assign responsibility.
A particularly nuanced risk is the dual-edged nature of XAI itself, specifically its potential to inadvertently reveal vulnerabilities. The same transparency that XAI provides to defenders to help them understand and trust their systems could, if compromised, be exploited by sophisticated adversaries. If attackers gain access to detailed explanations of how a security AI detects threats, they could potentially reverse-engineer the system’s logic, identify its blind spots, or craft bespoke attacks designed to evade detection. This “explainability paradox” means that the security of the XAI mechanisms and their outputs becomes paramount.
Addressing these multifaceted ethical implications requires more than purely technical solutions. It necessitates a socio-technical approach, involving ongoing dialogue and collaboration between AI developers, cybersecurity practitioners, ethicists, legal experts, policymakers, and the end-users of these systems. Establishing robust governance frameworks, ethical guidelines, and continuous oversight mechanisms is essential to ensure that XAI is developed and deployed responsibly in the cybersecurity domain.
Table 2 outlines some key ethical considerations and potential mitigation strategies.
Table 2: Ethical Considerations and Mitigation Pathways for XAI in Cybersecurity
| Ethical Concern | Manifestation in XAI for Cybersecurity | Proposed Mitigation/Best Practice |
|---|---|---|
| Algorithmic Bias leading to Profiling/Discrimination | AI models unfairly flagging individuals/groups based on biased training data or flawed model logic. | Diverse & representative training data; regular bias audits using XAI; fairness-aware ML techniques; human oversight in critical decisions. |
| Privacy Infringement via Explanations | Explanations revealing sensitive personal data or confidential operational details. | Differential privacy in explanation generation; secure access controls for XAI outputs; data minimisation; anonymisation/pseudonymisation techniques. |
| Accountability Gaps in Automated Decisions | Difficulty assigning responsibility when an XAI-guided decision results in harm or error. | Clear human oversight structures; defined responsibility frameworks for AI deployment and use; robust logging and auditing of AI decisions and explanations. |
| Transparency Illusions / Misleading Explanations | XAI providing explanations that are plausible but not faithful to the model’s true reasoning, or are too complex. | Robustness checks for explanation methods; user education on XAI limitations; techniques to assess explanation fidelity; iterative design with user feedback. |
| Potential for Malicious Use of Explanations | Adversaries exploiting XAI outputs to understand and circumvent security systems. | Secure storage and access control for XAI systems and explanations; red teaming of XAI-augmented systems; designing explanations with appropriate abstraction levels. |
| Lack of Contestability / Recourse | Individuals or entities adversely affected by an AI decision may lack clear mechanisms to challenge it. | Establishing clear procedures for appealing AI-driven security decisions; providing understandable explanations to affected parties. |
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V. Future Trajectories and the University of Latvia’s Research Outlook
As Explainable AI continues to mature, its trajectory in cybersecurity is pointing towards increasingly sophisticated, human-aligned, and integrated solutions. The research community, including institutions like the University of Latvia, is poised to contribute significantly to these future developments, addressing both enduring challenges and emerging opportunities.
A prominent future trend is the drive towards Human-Centric XAI. This involves moving beyond explanations that are merely technically accurate to those that are cognitively aligned with how human security analysts think, reason, and make decisions under pressure. The aim is to create XAI systems that offer more intuitive, interactive, and personalised explanations, tailored to the user’s expertise level, current task, and cognitive load. This approach considers human factors, behavioural indicators, and physiological aspects to enhance the human-AI collaboration, making security operations more seamless and effective. The ultimate goal is not just to make AI explainable, but to make AI-driven security truly usable and effective by ensuring its seamless integration into human workflows and cognitive processes.
Another key direction involves exploring the synergies between XAI and next-generation security architectures. As cybersecurity paradigms evolve towards more dynamic and adaptive models, such as Zero Trust Architecture (ZTA), XAI becomes increasingly vital. ZTA operates on the principle of “never trust, always verify,” dynamically assessing trust levels and enforcing granular access controls based on real-time context and behaviour. XAI can provide the crucial rationale behind these dynamic trust assessments and automated access control decisions, making these inherently complex and adaptive systems more understandable, manageable, and auditable for security administrators. This role of XAI is not merely for human oversight but is becoming fundamental for the design, debugging, validation, and trustworthy operation of these sophisticated, self-governing security systems.
Emerging research avenues are also capturing attention. The advent of quantum computing presents both new opportunities and significant threats to cybersecurity. Research into XAI for the security of quantum computing systems and, conversely, quantum-resistant XAI algorithms, will be critical as quantum technologies mature. Federated learning, which enables AI models to be trained on decentralised data sources while preserving privacy, is another area where XAI can play a role in explaining the behaviour of distributed security models and ensuring their trustworthiness. Furthermore, the rapid advancements in Large Language Models (LLMs) offer new possibilities for generating natural language explanations for complex AI decisions in cybersecurity, potentially making insights more accessible to a broader range of security personnel. The CASCON 2025 conference, for example, is anticipated to feature discussions on trustworthy AI and quantum computing, reflecting the timeliness of these research directions.
The University of Latvia is committed to pioneering research in these vital areas of trustworthy AI and resilient cybersecurity. The work undertaken by its Faculty of Computing in fields like XAI directly aligns with Latvia’s national cybersecurity strategy, which prioritises enhancing cyber defence capabilities, fortifying resilience against cyber assaults, and fostering public awareness and research initiatives. These national goals are, in turn, consonant with broader European Union objectives for digital security, critical infrastructure protection, and technological sovereignty. For the University of Latvia, a dedicated focus on XAI research, particularly in domains such as the protection of critical infrastructure and the development of human-centric approaches, presents a strategic opportunity. Such a focus not only addresses pressing national and regional security needs but also fosters innovation, attracts international research collaboration, and enhances the University’s standing as a centre of excellence in cutting-edge computer science research. This strategic alignment can lead to impactful contributions that bolster both national security and the global scientific community’s understanding of trustworthy AI.
VI. Conclusion: Towards a Transparent and Secure Digital Future
The cybersecurity landscape of 2025, characterised by AI-augmented threats of unprecedented sophistication, demands equally advanced and intelligent defensive measures. This paper has argued that Explainable AI (XAI) is not merely a desirable enhancement but an indispensable component for navigating this complex paradigm. XAI offers the crucial capability to transform AI from an opaque, “black box” tool into a transparent, understandable, and trustworthy partner in the perpetual endeavour to secure our digital ecosystems. By providing insights into the decision-making processes of AI systems, XAI empowers human analysts, improves the efficacy of security tools, facilitates regulatory compliance, and fosters the necessary confidence for effective human-AI collaboration.
However, the journey towards fully realised XAI in cybersecurity is ongoing and multifaceted. It requires sustained interdisciplinary research and collaboration. Advancing XAI capabilities necessitates a concerted effort that spans not only computer science and AI but also draws upon expertise from ethics, psychology, law, and human factors engineering. International cooperation between academic institutions, industry stakeholders, and governmental bodies is paramount for sharing knowledge, developing common standards and evaluation metrics, and addressing the global nature of cyber threats. The University of Latvia, with its commitment to international collaboration and research excellence, is well-positioned to contribute to this global effort.
Ultimately, the pursuit of explainable AI in cybersecurity is fundamental to building a digital future that is not only technologically advanced but also secure, understandable, and ethically sound. As AI systems become increasingly integral to all aspects of society, ensuring their transparency and trustworthiness is essential for maintaining human agency and control. The work in XAI is a critical step towards realising a future where technological advancements serve humanity reliably and ethically. The University of Latvia remains dedicated to this vision, contributing through rigorous research and education to the development of AI systems that inspire confidence and enhance our collective digital security.
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