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'AIaaS' and the enhancement of Information Systems Auditing

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The proposal to adopt Artificial Intelligence (possibly ‘as a Service’) within the context of Information Security, corporate governance and compliance is part of a modern and proactive vision of Risk Management.
The underlying idea is to use artificial intelligence not merely as an automation tool, but as a true enabler of a new operational model: more agile, flexible and effective in preventing and counteracting deviations in industrial and organizational processes.

In particular, the integration of AI makes it possible to move from a traditionally reactive approach to a predictive one, capable of intercepting deviations, anomalies and critical behaviors that would normally escape verification methods based on sampling or manual controls.
AI is employed to support assessments, strengthen risk management, optimize Remediation Plans and assist process audits within the key functions of the Organization (Compliance, Security, Risk Management, Revenue Assurance), ensuring broader and more effective coverage of control activities.
A distinctive element of the proposal is its dual approach:

on the one hand, the deployment of ‘AI-enabled’ devices and software to consolidate compliance oversight;
on the other hand, the development of innovative methods and tools to verify and ensure the ethical and regulatory compliance of the artificial intelligence systems themselves. This latter axis directly addresses European Union guidelines on trustworthy AI audits and aims to strengthen user trust in technologies that, to be effective, must also be transparent, controllable and designed according to durable ethical principles.

The value of AI in this context does not lie in replacing human judgment, but in enhancing it: Artificial Intelligence operates alongside auditors and governance professionals, supporting the decision-making process through “assisted” cognitive learning models, capable of generalizing knowledge from past experience and predicting the outcomes of specific behaviors with greater accuracy.
The adoption of this approach, combined with an ‘as a Service’ capability for Security Governance, delivers significant economic benefits even in the short term:

Reduction of operational costs: automation of assessments and predictive analyses can reduce the time devoted to manual audit activities, freeing professional resources for higher value-added tasks.
Lower losses due to Non-Compliance: timely prevention of anomalies and compliance violations reduces potential penalties and remediation costs, resulting in significant savings compared to traditional sampling-based approaches.
Efficiency of Remediation Plans: optimization of follow-up activities (which determine whether Management has adopted appropriate corrective measures to address identified deficiencies) and action plans leads to shorter resolution times for non-conformities, with a corresponding strengthening of business continuity.
Scalability and pay-per-use: the AIaaS model would allow costs and resources to be adjusted, avoiding high upfront investments and transforming expenditure into an operating cost aligned with actual usage.

Within this scenario, it is possible to provide a concrete response to the growing demands for Transparency, Security and System Control, through a holistic view of the organization as a dynamic ecosystem. It therefore represents not only a technological innovation (see, in this regard, the Artificial Intelligence Act), but also a cultural transformation in the very conception of corporate governance, as well as a strategic lever to improve process efficiency, reduce total cost of ownership and ensure a measurable and sustainable economic return.

Integrative methodological note

The evolution of Artificial Intelligence as a Service (‘AIaaS’) models, beyond constituting a technological or infrastructural transformation, represents – more profoundly – a change in the way organizations observe, govern and control their information and decision-making systems. In this sense, AIaaS becomes a methodological enabler for a new generation of Information Systems Auditing, capable of operating in contexts characterized by complexity, interdependence and continuous risk variability.

Auditing activity should no longer be conceived as a linear, static and predominantly ‘ex post’ process. On the contrary, its methodological value emerges with particular clarity through a paradigm configured as a ‘double-feedback logical circuit’ (counter-reacted control, represented by the ‘self-reinforcement with self-balancing’ scheme), in which the following coexist:

an internal operational cycle, oriented toward control, monitoring and verification of compliance and security parameters. Therefore: detection, alerting, assisted intervention;
an external learning cycle, devoted to the review of rules, policies, risk assumptions and decision criteria that guide the entire system. Therefore: aggregated analysis, model validation, rule updating;

allowing the calibration not only of parameters but also of the rules that govern control processes. Within this architecture, AIaaS provides the necessary support for this dual circuit to operate in a continuous, scalable and sustainable manner.

Artificial Intelligence services, delivered as shared and modular capabilities, indeed make it possible to:

collect and correlate weak signals coming from heterogeneous domains (technical, organizational, behavioral);
transform large volumes of raw data into interpretable indicators, useful both for operational (immediate) control and for strategic reflection;
support adaptive decision-making processes, while keeping the auditor and management within the responsibility loop (‘human-in-the-loop’).

In this model, AI is not meant to “decide in place of” the human being, but to strengthen human perception, evaluation and learning capabilities, reducing the gap between expected quality and the quality actually achieved by the audited processes. This leads to a conception of Information Systems Auditing as a dynamic process, in which:

compliance is not a static attribute, but a condition to be maintained over time;
risk is not only to be mitigated, but to be understood and anticipated;
control is not limited to verification, but includes the ability to critically review the rules that govern it.

The methodological contribution of ‘AIaaS’ develops along several additional key points:

Explainability and ‘auditability by design’ – Every output entering the decision perimeter must be accompanied by clear and traceable artifacts/evidence (relevant features, model version, reference dataset, timestamp, digital signature), so as to enable ‘ex post’ validation by the auditor and protect professional accountability.
Dynamic risk map as an operational dashboard – The proposed dynamic map constitutes the “single source of truth” for AIaaS: it visualizes deviations between expected and observed states across operational, regulatory and reputational dimensions, guiding action selection in the (internal) operational cycle and strategic updates in the external cycle.
Continuous validation and 'bias' management (cognitive distortions) – AIaaS services shall include structured validation procedures (retrospective verification on historical data, cross-validation, prior predictive checking) and fairness metrics, together with monitoring policies and mitigation plans for each identified “drift.”
Phased approach and risk minimization – Rollout (deployment) should begin with limited scopes and non-sensitive or pseudonymized data, progressively extending only after verifying MTTD (MeanTimeToDetect), MTTR (MeanTimeToRespond), detection rate and operational cost/benefit.

Within this framework, the integration between AIaaS and auditing fosters an approach oriented toward organizational resilience, consistent with what is illustrated in the Judo-BI^TM Proof of Concept, while preserving human responsibility and regulatory compliance.
Just as in the martial analogy effective control arises from dynamic balance and the ability to adapt to imbalance, likewise modern auditing can benefit from tools that enable:

early interception of deviations and vulnerabilities;
progressive calibration of responses;
adaptation of policies, controls and interpretative models to a changing context.

It is important to emphasize that such methodological evolution requires, as a necessary condition, solid safeguards of ethical governance, transparency and accountability.
The adoption of AIaaS in auditing implies the clear definition of roles, scopes of use, model validation criteria and output interpretation modalities, so that Artificial Intelligence remains a reliable support tool and not an opaque source of uncontrolled automatisms.

In conclusion, the integration between AIaaS and Information Systems Auditing, viewed through the lens of the double-feedback model, makes it possible to outline a methodology that does not aim to replace professional judgment, but to enhance it, making auditing more continuous, adaptive and capable of learning. A methodology that, consistently with the conceptual framework presented here, interprets control not as a rigid constraint, but as a living process of regulation and system growth.

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( last reviewd on Mar. 2026 )

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