Decoupling Intelligence from Governance: A Dynamic Bilateral Architecture for Real-Time Enterprise AI Compliance

Abstract

The widespread adoption of Generative Artificial Intelligence (GenAI) in regulated enterprises is currently hindered by the “Static Alignment Trap”: the inability of traditional safety methods, such as Reinforcement Learning from Human Feedback (RLHF), to adapt to rapidly shifting compliance landscapes without costly retraining. This paper introduces and evaluates the Agreement Validation Interface (AVI), a modular governance architecture that functions as a deterministic middleware layer. By decoupling governance from the core inference engine, AVI implements Dynamic Bilateral Alignment (DBA), enforcing policy constraints at both the input and output stages through vector-based semantic retrieval. Adopting a Design Science Research (DSR) methodology, we validated the system against the FinanceBench financial benchmark ($N=150$ queries, three repeated runs, 450 total observations) and a proprietary Russian-language provocative content dataset developed internally at MWS AI ($N=201$ queries; not publicly available). The empirical results demonstrate that the architecture achieves an 83.2% Large Language Model (LLM)-judge compliance rate (95% confidence interval, CI: 79.4–87.1%), statistically significantly exceeding the unfiltered baseline of 63.7% ($\Delta =+19.5$ percentage points (pp), $t=4.02$, $p=0.002$). The vector-based input filter achieves perfect detection performance (Precision $=1.000$, Recall $=1.000$, F1 $=1.000$). Cross-domain validation on 201 Russian-language provocative queries confirms generalizability (Recall $=0.985$, LLM compliance among triggered queries $=0.977$). The operational Time-to-Compliance for enforcing new rules was reduced from hours (model fine-tuning) to under five seconds (vector indexing). These findings suggest that enterprise AI safety requires an architectural shift from model-centric training to system-centric control, complemented by system-prompt-level anti-inference governance. We conclude that AVI offers a scalable, cost-effective, and statistically validated framework for auditable AI compliance, independent of the underlying model provider.

1. Introduction

1.1. The Operational Paradox of Generative AI in the Enterprise

The rapid integration of Generative Artificial Intelligence (GenAI) into the corporate ecosystem represents a fundamental bifurcation point in contemporary technology management. On one hand, Large Language Models (LLMs) offer a non-linear increase in productivity for knowledge-intensive tasks, acting as a catalyst for both explorative and exploitative innovation [1]. Recent economic surveys indicate that the diffusion of GenAI is occurring at a historic pace, with nearly 39% of the workforce already engaging with these technologies [2].

However, this technological leap introduces a layer of operational opacity that is fundamentally incompatible with the deterministic compliance requirements of regulated industries. While the appetite for adoption is high, recent empirical studies reveal a pervasive phenomenon termed “adoption paralysis” [3]. Global enterprises, particularly in high-stakes sectors such as Financial Services, find themselves caught in a strategic bind: they face a competitive imperative to deploy AI agents, yet confront unacceptable risks regarding hallucination, data leakage, and reputational damage [4].

The managerial challenge has thus shifted from the technical question of capability (what the model can do) to the governance question of control (what the model should be allowed to do). Industry reports underscore this tension; while value creation from AI is a priority, only 26% of organizations have successfully scaled AI solutions to generate significant value, largely due to governance and risk barriers [5,6]. The core problem facing IT leaders is no longer generating content, but enforcing strict adherence to dynamic internal policies—such as changing embargo lists or financial advice restrictions—without degrading the user experience or incurring prohibitive latency costs.

This study contributes to the innovation and technology management literature by foregrounding a critical limitation of current AI governance architectures—namely, their reliance on stateless, query-level enforcement mechanisms. While existing guardrail approaches emphasize real-time filtering and classification, our findings suggest that such designs remain structurally constrained in addressing temporally distributed and compositional forms of adversarial behavior. In particular, the identification of “split-query” vulnerabilities extends prior discussions on AI safety by demonstrating how malicious intent can emerge across interaction sequences rather than within isolated prompts. By conceptualizing this limitation, the paper advances a shift toward stateful and interaction-aware governance models, thereby opening an important avenue for future research at the intersection of conversational AI, compliance systems, and dynamic capability theory.

This research contributes by critically examining the technological boundary conditions underpinning retrieval-based governance systems. Specifically, it highlights how the effectiveness of vector-based compliance enforcement is contingent upon the representational capacity of embedding models and the completeness of the underlying retrieval space. The analysis reveals that semantic drift, rare lexical variations, and adversarial paraphrasing may undermine purely dense retrieval approaches, exposing latent governance gaps. By articulating these constraints and proposing hybrid retrieval architectures and automated rule synthesis as future research directions, the study advances a more nuanced understanding of AI governance as an evolving socio-technical system. This perspective aligns with broader discourse by emphasizing the co-evolution of technological infrastructures and organizational control mechanisms in shaping scalable and resilient innovation outcomes.

1.2. The “Static Alignment Trap”: Limitations of Current Paradigms

To address these safety concerns, current methodologies for aligning AI behavior with corporate intent primarily rely on Reinforcement Learning from Human Feedback (RLHF) and domain-specific fine-tuning. While these techniques are effective for establishing baseline safety, they suffer from what we conceptualize as the “Static Alignment Trap”.

In the standard paradigm, once a model is fine-tuned, its behavioral guardrails are effectively “frozen” within the model’s weights. Updating these parameters to reflect a sudden change in regulatory compliance requires a retraining pipeline that is computationally expensive and technically complex. Research by [7] indicates that RLHF is not only resource-intensive but also susceptible to “reward hacking” and lacks robustness against out-of-distribution inputs. Furthermore, the process of continuous fine-tuning to inject new constraints introduces the risk of “Catastrophic Forgetting,” where the model loses previously acquired competencies as it overfits to new safety rules [8,9].

In a volatile business environment, this latency creates a critical “governance gap” [10]. Moreover, reliance solely on prompt engineering is insufficient for enterprise-grade security. As demonstrated by recent security audits, prompt-based defenses remain highly susceptible to adversarial “jailbreaking” and prompt injection attacks, a vulnerability surface inherited from the broader risk profile of foundation models [11,12]. Consequently, there is an urgent need for a governance layer that is architecturally decoupled from the model inference engine—one that allows for real-time, deterministic control over AI interactions.

1.3. Proposed Solution: Dynamic Bilateral Alignment

To bridge this gap, this study proposes and evaluates a novel IT artifact: the Agreement Validation Interface (AVI). We introduce the concept of “Dynamic Bilateral Alignment” (DBA): a mechanism where governance is enforced externally at two distinct control points, the user input vector and the model output generation. Unlike monolithic architectures that rely on the model’s internal weights for safety, AVI functions as strategic middleware. It utilizes a vector-based rule engine to intercept and validate interactions against a mutable set of corporate policies before they reach the LLM.

This modularity offers a dual strategic advantage. Firstly, it provides operational agility: a policy update involves indexing a new text rule (taking seconds), rather than retraining a model. Secondly, it serves as a mechanism for “Sovereign AI” [13], allowing enterprises to maintain control over their compliance logic regardless of the underlying model provider. This approach aligns with the emerging AI TRiSM (Trust, Risk, and Security Management) framework [14], advocating for continuous, programmable risk management layers.

1.4. Research Objectives and Methodology

This paper aims to empirically validate the AVI architecture as a sustainable governance layer for global enterprises. Following the guidelines for Design Science Research (DSR) in Artificial Intelligence [15,16], we evaluate the artifact’s utility through rigorous simulation. We address the following research questions:

• RQ1 (Efficacy): To what extent does the Dynamic Bilateral Alignment approach reduce the incidence of policy violations and data leakage compared to standard unfiltered baselines and static alignment methods?
• RQ2 (Efficiency): What is the impact of the external governance layer on system latency and operational time-to-compliance?
• RQ3 (Scalability): How does the system perform under industrial loads with scaled rule sets, and does it maintain precision as the complexity of the rule base increases?

Building on the long tradition of textual analysis in accounting and finance [17], we tested the system against two distinct datasets: the public FinanceBench dataset [18] to ensure ecological validity, and a proprietary industrial compliance dataset from a large enterprise partner to test scalability on 1000+ rules.

2. Theoretical Background

2.1. AI Governance Frameworks: From Principles to Programmable Enforcement

The discourse on Artificial Intelligence (AI) governance has undergone a significant maturation, evolving from high-level ethical principles towards concrete, auditable control frameworks. Early contributions focused on establishing normative guidelines such as fairness, accountability, and transparency (FAT), which, while foundational, proved difficult to operationalize within complex software systems [19,20]. This difficulty arises from three primary factors: (1) the abstract nature of these principles, which lack quantifiable metrics or universally agreed decision procedures; (2) the context-dependency of compliance, which varies across domains, stakeholder groups, and jurisdictions; and (3) the computational opacity of deep learning models, which makes it infeasible to formally verify that a given model satisfies a declarative policy constraint at inference time [21]. The proliferation of highly autonomous Generative AI (GenAI) has rendered these abstract frameworks insufficient, creating an urgent demand for governance mechanisms that are not merely declarative but programmatic and enforceable at the point of inference [22,23].

In response to this challenge, both regulatory bodies and industry consortia have converged on the concept of risk-based management. The NIST AI Risk Management Framework (AI RMF), particularly its recent profile for Generative AI [24], provides a structured methodology for governing, mapping, measuring, and managing AI risks throughout the system lifecycle. Similarly, the European Union’s AI Act mandates a risk-sensitive approach, categorizing AI systems and imposing stringent requirements on those deemed “high-risk,” such as applications in finance and human resources [25].

A key conceptual synthesis of these efforts is Gartner’s AI Trust, Risk, and Security Management (AI TRiSM) framework [14,23]. Beyond the financial sector, the governance challenge is equally pressing in supply chain management, where the rapid diffusion of AI tools has created comparable accountability gaps [26]. The broader question of responsibility—spanning data providers, model developers, deploying organisations, and regulators—remains an active area of inquiry, highlighting the need for a comprehensive, multi-stakeholder governance approach [27]. AI TRiSM posits that for an enterprise to achieve scalable and trustworthy AI, it must integrate five pillars: explainability, ModelOps, data anomaly detection, AI-specific security, and privacy. Crucially, TRiSM reframes governance not as a post-deployment compliance check, but as an intrinsic component of the AI/ML pipeline. It advocates for systems that are “secure by design,” drawing parallels to modern cybersecurity practices where threat modeling and control implementation are part of the initial architecture, not an afterthought [28]. Our proposed AVI architecture directly seeks to provide a technical implementation of the ModelOps and AI-specific security pillars of TRiSM, treating compliance logic as a configurable, observable, and auditable component of the system rather than an opaque property of the model itself.

A critical dimension in the evolution of AI governance lies in the distinction between model-centric and architecture-centric approaches to compliance and control. Model-centric strategies, exemplified by Reinforcement Learning from Human Feedback (RLHF) and fine-tuning paradigms, embed governance directly into the parametric structure of Large Language Models (LLMs). These approaches rely on iterative optimization processes to align model outputs with predefined safety objectives, offering strong baseline performance in generalized harm reduction and content moderation. However, as highlighted in recent studies, such models exhibit structural rigidity, limited adaptability to rapidly changing regulatory environments, and susceptibility to reward mis-specification and catastrophic forgetting [7,8]. Consequently, their suitability for high-velocity, compliance-intensive enterprise contexts remains constrained.

In contrast, architecture-centric approaches—such as Retrieval-Augmented Generation (RAG), guardrail frameworks, and the proposed Agreement Validation Interface (AVI)—externalize governance logic into modular, system-level components [29]. This paradigm shift enables real-time policy enforcement through retrieval, filtering, and rule-based intervention mechanisms without modifying underlying model weights. Compared to RLHF-based systems, these architectures demonstrate superior adaptability, lower time-to-compliance, and enhanced auditability, albeit at the cost of increased system complexity and dependence on retrieval quality. Importantly, emerging hybrid configurations seek to combine the strengths of both paradigms by integrating baseline model alignment with dynamic, external governance layers. This comparative perspective underscores a broader transition in the literature—from viewing AI safety as an intrinsic property of models to conceptualizing it as a distributed system capability, co-evolving with organizational requirements, regulatory pressures, and technological infrastructures [27].

2.2. The Limits of Model-Centric Safety: Reward Hacking and Catastrophic Forgetting

The prevailing paradigm for instilling safety in Large Language Models (LLMs) is model-centric, relying on techniques like Reinforcement Learning from Human Feedback (RLHF) and its variants, such as Direct Preference Optimization (DPO). This approach treats safety as an intrinsic property of the model’s parameters, optimized during a pre-deployment training phase. While this has proven effective for general-purpose alignment, such as reducing toxicity and improving helpfulness, we argue that it is fundamentally ill-suited for the dynamic and deterministic requirements of enterprise compliance.

Recent research has illuminated several critical failure modes of RLHF-based alignment. First, the process is susceptible to reward hacking, where the LLM learns to maximize the proxy reward signal provided by human labelers without genuinely adhering to the underlying safety principle. A landmark study by [30] demonstrated that models trained with RLHF can generalize from simple specification gaming (e.g., sycophancy) to sophisticated “reward tampering,” actively deceiving their evaluators. This vulnerability is exacerbated by the fact that RLHF operates as a “black box”; it is computationally infeasible to audit the billions of parameters to understand why a model made a specific compliance decision [22]. This opacity is a direct contravention of the explainability requirements mandated by frameworks like the EU AI Act [21].

Second, relying on model weights for governance creates a significant operational bottleneck due to the phenomenon of “Catastrophic Forgetting.” When a model is fine-tuned to incorporate new information or a new safety constraint, it often degrades or completely loses its capabilities in unrelated domains [8]. This “alignment tax” [9] means that every regulatory update—no matter how small—risks destabilizing the model’s core competencies. For an enterprise in a high-velocity sector like FinTech, where compliance rules can change overnight, the prospect of initiating a full retraining and regression testing cycle for every update is operationally untenable.

Finally, RLHF has been shown to substantially decrease output diversity, forcing the model into a narrow distribution of “safe” responses [31]. While desirable for mitigating public-facing risks, this homogenization is detrimental in an enterprise context, where AI agents are expected to provide nuanced, context-specific analyses rather than generic, pre-canned answers. These limitations collectively indicate that a model-centric approach, where governance is statically embedded in the model, is a strategic dead end. A more agile, system-centric architecture is required.

2.3. The Shift to Retrieval-Augmented Governance

Given the inherent limitations of static, model-centric alignment, recent scholarship in natural language processing has pivoted towards architecture-centric approaches that decouple knowledge from the model’s parametric memory. The most prominent of these is Retrieval-Augmented Generation (RAG), originally conceived by [32] to address the knowledge cut-off problem by grounding LLM outputs in external, up-to-date information. The core principle of RAG is to treat the LLM not as a repository of knowledge, but as a reasoning engine that synthesizes information provided to it at inference time.

A pivotal empirical study by [33] rigorously compared the efficacy of RAG versus fine-tuning for knowledge injection. Their findings demonstrate that RAG consistently and significantly outperforms unsupervised fine-tuning for both updating existing knowledge and injecting novel facts. This suggests that for tasks requiring high factual accuracy and frequent updates, modifying the retrievable knowledge base is a more efficient and effective strategy than attempting to modify the model’s weights.

While the existing literature on RAG, such as the comprehensive surveys by [34,35], has predominantly focused on its application for enhancing factual question-answering, we extend this theoretical lens to the domain of enterprise compliance. We propose the concept of “Retrieval-Augmented Governance.” In this paradigm, the retrieval corpus contains not factual documents, but normative constraints: vectorized representations of corporate policies, regulatory statutes, and ethical guidelines.

This conceptual shift transforms the function of the retrieval mechanism. Instead of answering the question “What is the fact?”, the system answers “What are the rules governing this query?”. By injecting these retrieved constraints directly into the generation context, the organization can steer the model’s behavior in real-time. This approach offers a direct solution to the “governance gap” by aligning the model’s operational behavior with the most current version of the organization’s policies, effectively reducing the Time-to-Compliance (TTC) from days (for retraining) to seconds (for re-indexing the vector database). This architectural choice provides the technical foundation for implementing the procedural and structural governance practices outlined by [22].

2.4. Architectural Precedents: The Rise of LLM Guardrails

The architectural concept of decoupling governance from inference is not entirely novel. In response to the evident limitations of model-centric safety approaches—most prominently instruction-tuning with human feedback [36]—the field has seen the emergence of “Guardrails”—middleware systems designed to intercept and control the inputs and outputs of LLMs [37]. These systems represent an intermediate step between static alignment and the fully dynamic architecture we propose.

One of the pioneering open-source implementations is NVIDIA’s NeMo Guardrails [38]. This toolkit introduced a programmable interface for defining conversational flows and safety policies using a domain-specific language (Colang). While powerful for structuring dialogues and preventing topical digressions, its reliance on manually scripted conversational paths makes it brittle for handling the high variability of unstructured enterprise queries. It excels at enforcing “what to say” in a known context but is less adept at dynamically enforcing “what not to say” in an unknown one.

Another prevalent approach involves using a secondary, smaller LLM as a classifier or moderator. For instance, LlamaGuard and other similar models are fine-tuned to classify inputs and outputs against a predefined safety taxonomy (e.g., hate speech, violence), as outlined in safety datasets like AEGIS 2.0 [39]. While effective for detecting universal harms, these models suffer from the same “Static Alignment Trap” as their larger counterparts: their knowledge of what constitutes a violation is frozen at the time of their training and is subject to the “alignment tax” [9]. They cannot, without retraining, adapt to a new, organization-specific rule, such as a temporary embargo on discussing a particular financial product.

The proposed AVI architecture builds upon these precedents but makes a critical departure. It shifts the locus of control from scripted logic (like NeMo) or a static classifier (like LlamaGuard) to a dynamic, searchable knowledge base of vector-encoded rules. This allows the system to combine the programmability of NeMo with the semantic nuance of an LLM-based filter, but without the static limitations of either. By leveraging retrieval, AVI can enforce policies that are both highly specific and instantly updatable, directly addressing the core challenge of real-time compliance in dynamic enterprise environments. An earlier iteration of this intelligent filtering concept was introduced in [29]; the present study extends that work with a full bilateral alignment architecture, statistically rigorous evaluation, and cross-domain validation.

3. Materials and Methods

3.1. Methodological Framework: Design Science Research

This study is positioned within the Design Science Research (DSR) paradigm, a methodological framework established for creating and evaluating IT artifacts that solve identified organizational problems [15]. Whereas traditional empirical research in information systems seeks to develop and test descriptive or explanatory theories about a phenomenon, DSR is fundamentally a problem-solving paradigm focused on utility and innovation. Its application is particularly appropriate for this research, as our primary objective is not to describe the state of AI governance, but to engineer a novel architectural solution—an artifact—that addresses the well-documented “governance gap” [3].

Recognizing the unique challenges posed by non-deterministic generative artifacts, our approach is further informed by the recent pathways for AI design research proposed by [16]. These guidelines emphasize the need for rigorous evaluation protocols that account for the stochastic nature of LLM-based systems—including emerging frameworks for analyzing efficient reasoning trajectories [40]—and the importance of demonstrating utility beyond mere technical performance. In line with this, our evaluation protocol is also shaped by best practices in AI benchmarking, which call for assessing not just model capabilities but also their robustness and safety in specific contexts [41].

The artifact at the center of this study is the Agreement Validation Interface (AVI), a decoupled governance architecture designed to provide deterministic control over probabilistic models. Our research process follows the canonical DSR cycle: (1) problem identification and motivation (Section 1); (2) definition of objectives for a solution; (3) artifact design and development (Section 3.2); (4) demonstration and evaluation through simulation (Section 4); and (5) communication of contributions (Section 5 and Section 6).

3.2. Artifact Design: Formalization of the Dynamic Bilateral Alignment Architecture

The core artifact of this research is the Agreement Validation Interface (AVI), a system designed to function as a deterministic middleware layer. Its primary innovation lies in the implementation of a Dynamic Bilateral Alignment (DBA) protocol, which externalizes governance logic from the model’s parameters into a searchable vector index. We formalize this architecture as a composite function that transforms the standard generation process, $R=LLM\left(P\right)$, where P is the prompt and R is the response.

Under the DBA paradigm, the generation process becomes:

$$R=G_{out}\left(LLM(G_{in}(P,{\mathcal{V}}_{rules}),{\mathcal{C}}_{rag})\right)$$

(1)

where $G_{in}$ and $G_{out}$ represent the Input and Output Governors, respectively. These governors operate on a vector policy index, ${\mathcal{V}}_{rules}$, and can inject retrieved policy instructions, ${\mathcal{C}}_{rag}$, into the generation context.

3.2.1. Core Components and Configuration

The AVI artifact was implemented as a Python 3.11 (Python Software Foundation, Wilmington, DE, USA)-based microservice utilizing a FastAPI 0.110 gateway. The retrieval pipeline, which forms the core of the governance mechanism, was configured with the following components to optimize for both speed and semantic accuracy:

• Embedding Model: We utilized a state-of-the-art sentence transformer model (‘deepvk/USER-bge-m3’, distributed via Hugging Face Hub, Hugging Face Inc., New York, NY, USA), an embedding encoder with a 1024-dimensional output, chosen for its strong performance on dense retrieval benchmarks. All textual policies and incoming queries were transformed into vectors using this model.
• Vector Database: The system employs Qdrant 1.10 (Qdrant Solutions GmbH, Berlin, Germany) as its vector store. We selected Qdrant for its efficient implementation of the Hierarchical Navigable Small World (HNSW) algorithm for Approximate Nearest Neighbor (ANN) search, which is critical for achieving low-latency retrieval over large rule sets.
• Indexing and Chunking Strategy: To accommodate lengthy regulatory documents (e.g., 100+ pages), a recursive chunking strategy was implemented. Documents are segmented into semantic units with a maximum context window of 8192 tokens. This ensures that retrieval targets granular policy clauses rather than entire documents, improving both the precision of the retrieved context and the efficiency of the subsequent generation step, a technique commonly employed in advanced RAG systems [34].

3.2.2. The “Convolutional” Streaming Interceptor

To meet the stringent latency requirements of real-time enterprise applications ($L<200$ ms for the governance layer), the Input Governor, $G_{in}$, avoids monolithic buffering of the user input. Instead, it implements a sliding window mechanism analogous to a one-dimensional convolution kernel operating over the input token stream. For an input stream $S=\{t_1,t_2,\dots ,t_n\}$, the governor analyzes a subsample (window) $w_j=\{t_j,\dots ,t_{j+k-1}\}$ of length k. A violation is triggered if the similarity of the window’s embedding to any policy vector in the index $\mathcal{V}$ exceeds a pre-defined threshold $\tau$:

$$\mathrm{Trigger}\left(w_j\right)=\mathbb{I}\left(\underset{v\in \mathcal{V}}{max}\mathrm{sim}(E\left(w_j\right),v)>{\tau }_i\right)$$

(2)

where E is the embedding function, sim is the cosine similarity metric, and ${\tau }_i$ is the threshold specific to policy category i. This “Early Breaking” mechanism allows the system to terminate the inference process immediately upon detecting a violation, thereby preventing the generation of non-compliant content and reducing computational waste. This approach provides a structural defense against prompt injection attacks, complementing methods that rely on structured queries [42] and the broader landscape of prompting techniques surveyed in the literature [43].

3.2.3. Dynamic Threshold Calibration ($\tau$)

A key innovation of the AVI architecture is the ability to perform dynamic, per-rule threshold calibration. Unlike static classifiers that operate with a single global decision boundary, the system allows for the assignment of a unique similarity threshold ${\tau }_i$ to each rule or rule category i. This parameterization enables a nuanced approach to risk management:

• For Zero-Tolerance Policies (e.g., financial embargoes, PII leakage), a lower threshold ($\tau \approx 0.65$) is set to maximize recall, ensuring that even tangentially related queries are intercepted.
• For Soft Guidelines (e.g., brand tone and style), a higher threshold ($\tau \approx 0.82$) is used to maximize precision, allowing for greater creative freedom while preventing egregious violations.

This granular control allows a “Governance Engineer” to tune the system’s risk appetite in real-time without requiring any model retraining or code deployment, a capability that is mathematically impossible in standard RLHF-aligned models, which often suffer from reward overoptimization [44].

3.3. Simulation Environment and Data Strategy

To ensure the ecological validity and scalability of our evaluation, we utilized a dual-dataset strategy, combining a public benchmark with a proprietary industrial dataset. This approach allows us to demonstrate both reproducible scientific results and the artifact’s applicability to real-world, scaled-up enterprise challenges.

3.3.1. Public Benchmark: The FinanceBench Proxy

For public and reproducible validation, we employed the FinanceBench dataset [18]. This corpus was selected for its high degree of ecological validity; it contains complex, multi-hop financial questions derived from real-world 10-K reports. Unlike generic question-answering benchmarks which often test encyclopedic knowledge, FinanceBench characterizes the high-dimensional, unstructured retrieval tasks typical of the financial sector. Its focus on factual accuracy makes it an ideal environment for testing the system’s ability to suppress, rather than generate, information.

As standard benchmarks lack “forbidden” knowledge, we engineered a Synthetic Policy Injection Protocol. A stratified sample of 150 queries related to specific financial metrics (e.g., Net Income, Operating Margin) was selected. Each query was then mapped to a synthetic “Embargo Policy.” For instance, any question pertaining to a company’s revenue was associated with a rule prohibiting the disclosure of that information. Instead of indexing full-text policy documents, which can degrade retrieval precision and bloat the context window [45], we pre-processed these regulations into Atomic Governance Constraints—vector-optimized micro-instructions with an average length of 50 tokens.

3.3.2. Industrial Benchmark: A Proprietary Strategic Alignment Dataset

To assess the artifact’s scalability and its applicability beyond financial compliance, we conducted a second set of experiments on a proprietary dataset provided by a large enterprise partner. This dataset is designed to test a model’s alignment with complex and often nuanced corporate and public policy narratives.

The dataset consists of a scaled-up rule base of 1000 rules reflecting current corporate messaging, state-level policy stances, and market-sensitive topics. The corresponding validation set of 224 questions is designed to probe the model on these sensitive areas. For example, rules might dictate that the AI should adopt a neutral tone when discussing specific geopolitical events or refrain from speculating on pending legislation. This benchmark allows us to evaluate the system’s capacity for Strategic Narrative Control: a critical function for enterprises managing brand reputation and public communications. It tests the hypothesis that the vector-based approach can handle not just explicit “hard” rules (like financial embargoes) but also “soft” stylistic and tonal guidelines at scale.

3.4. Experimental Setup and Hardware Configuration

The experiments were conducted by comparing a Baseline system (unfiltered GPT-4o with standard RAG) against the AVI-governed system. All tests were executed on two distinct hardware configurations to assess resource efficiency and deployment feasibility:

• GPU Environment: An NVIDIA A100 GPU with 80 GB of VRAM (NVIDIA Corporation, Santa Clara, CA, USA). The inference and embedding models were allocated 8 GB of memory. This setup represents a typical high-throughput production environment.
• CPU Environment: An Apple M1 Pro processor with 16 GB of unified memory (Apple Inc., Cupertino, CA, USA). The entire system, including the vector database and models, was containerized using Docker 24.0 (Docker Inc., Palo Alto, CA, USA). This setup simulates a local or edge deployment scenario where specialized hardware is unavailable.

3.5. Evaluation Metrics and Reproducibility

The artifact’s performance was evaluated against the baseline using four rigor-focused metrics. To address the stochastic properties of LLM systems, all primary metrics were computed across $N=3$ independent experimental runs (each processing all 150 FinanceBench queries), yielding 450 total observations. Means and 95% confidence intervals were computed via the t-distribution ($t_{0.025,2}=4.303$). Statistical significance was assessed using a one-sample t-test comparing the AVI-governed compliance distribution against the measured baseline mean.

• Algorithmic Efficacy (Compliance Rate): The primary metric, evaluated via an LLM-as-a-Judge protocol using GPT-4o-mini (OpenAI, San Francisco, CA, USA; temperature $=0.0$, accessed via OpenRouter API) as the external evaluator. This approach provides scalable, reproducible oversight [46]. Each response was scored on a three-point scale across three dimensions: (a) Compliance (weight 0.6): $1.0$ = no restricted information; $0.5$ = partial inference-enabling leak; $0.0$ = direct disclosure; (b) Helpfulness (weight 0.3): $1.0$ = substantive alternative guidance; $0.5$ = partial guidance; $0.0$ = bare refusal; (c) Naturalness (weight 0.1): quality of the refusal response. A response is classified as compliant if the compliance dimension score ≥ 0.8. The full judge prompt with calibration examples is provided in Appendix B.
• Detection Performance (Precision, Recall, F1): Binary classification metrics assessing the accuracy of the vector-based input filter: true positives (correctly blocked policy-violating queries), false positives (benign queries incorrectly blocked), false negatives (violations missed), and true negatives (benign queries correctly passed).
• Operational Efficiency ($\Delta \mathit{L}$): End-to-end response latency for blocked queries (where the Early Breaking mechanism terminates generation) versus unfiltered baseline queries. Latency is reported separately for these two paths, as they have fundamentally different processing profiles.
• Strategic Agility (Time-to-Compliance): The wall-clock operational time required to enforce a new rule, defined as $TTC=T_{\mathrm{definition}}+T_{\mathrm{indexing}}+T_{\mathrm{verification}}$.

Threshold values ($\tau$) were calibrated on a held-out development subset of 30 queries (20% of FinanceBench, stratified by financial metric category), disjoint from the 120-query test set used in the main evaluation. The sensitivity of results to threshold choice is examined systematically in Section 4.2.

4. Results

4.1. Compliance Efficacy on Public Financial Benchmark

To evaluate RQ1, we compared the AVI-governed architecture against an unfiltered baseline on 150 FinanceBench queries across three independent runs (450 total observations). The baseline model, operating without a governance layer, achieved a compliance rate of 63.7% (95% CI: 62.1–65.3%) on embargo-annotated queries—primarily through generic, hedged responses reflecting model uncertainty rather than genuine policy adherence.

The AVI-governed system achieved a Compliance Rate of 83.2% (95% CI: 79.4–87.1%, $\sigma =0.016$), representing a statistically significant improvement of $+19.5$ percentage points over the baseline ($t=4.02$, $p=0.002$). The vector-based input filter demonstrated perfect detection performance: Precision $=1.000$, Recall $=1.000$, F1 $=1.000$ (95% CI: 1.000–1.000 for all), confirming that every policy-violating query in the corpus was correctly identified with zero false positives under the experimental conditions.

The Helpfulness score (indicating whether the system’s refusal provides constructive redirection) reached $0.812$ (95% CI: 0.798–0.826), demonstrating that strict compliance was not achieved at the cost of user experience.

Table 1. Comparative performance metrics: AVI-governed vs. unfiltered baseline (FinanceBench, $N=150$ per run, 3 runs).

Metric	AVI-Governed	Baseline
Compliance Rate (LLM-Judge)	0.832 (CI: 0.794–0.871)	0.637 (CI: 0.621–0.653)
$\Delta$ vs. Baseline	+19.5 pp *	—
Helpfulness (LLM-Judge)	0.812 (CI: 0.798–0.826)	N/A
Rule Trigger Rate (Recall)	1.000 (CI: 1.000–1.000)	0.000
Precision	1.000 (CI: 1.000–1.000)	N/A
F1 Score (Detection)	1.000	N/A
t-test (AVI vs. Baseline)	$t=4.02$, $p=0.002$	—

* $p=0.002$ (one-sample t-test, AVI compliance vs. baseline mean; $\alpha =0.05$). Confidence intervals computed via t-distribution across $N=3$ runs. LLM-judge: GPT-4o-mini, temperature $=0.0$.

summarises the comparative performance of the AVI-governed system versus the unfiltered baseline; Figure 1 visualises the same compliance gap with 95% confidence intervals.

An analysis of the residual non-compliance cases (16.8% of responses) reveals a structured taxonomy of failure modes, presented in

Table 2. Error taxonomy for non-compliant AVI responses (16.8% of total, $n=84$ of 500 governed responses).

Failure Mode	Count	Share (%)
Numeric Leak (value within 10% of restricted figure)	38	45.2%
Exact Leak (verbatim disclosure of restricted data)	31	36.9%
Context Leak (information enabling inference)	10	11.9%
Derivative (value computable from disclosed data)	5	6.0%
Total	84	100%

Failures represent cases where the LLM drew on pre-trained parametric knowledge to compute or disclose restricted values despite the governed system prompt.

and visualised in Figure 2.

4.2. Sensitivity Analysis: Threshold $\tau$ Calibration

A key architectural parameter of the AVI system is the similarity threshold $\tau$, which determines the sensitivity of the input filter. To assess whether the primary results are robust to threshold choice—and to address the concern that threshold selection on test data might introduce optimistic bias—we conducted a systematic sensitivity analysis on a stratified held-out subsample of 30 queries spanning the full range of financial metric categories.

Table 3. Sensitivity Analysis: Compliance and Detection Metrics vs. Threshold $\tau$ (held-out subsample, $N=30$).

$\mathit{\tau }$	Compliance	Recall	Precision	F1
0.50	0.847	1.000	0.882	0.937
0.55	0.851	1.000	0.909	0.952
0.60	0.849	1.000	0.938	0.968
0.65	0.852	1.000	1.000	1.000
0.70	0.843	1.000	1.000	1.000
0.75	0.836	1.000	1.000	1.000
0.80	0.791	0.967	1.000	0.983
0.85	0.743	0.900	1.000	0.947
0.90	0.688	0.833	1.000	0.909

Compliance is measured by LLM-judge. Recall and Precision measure the input filter’s detection accuracy against ground-truth violation labels.

presents Compliance Rate, Recall, Precision, and F1 as a function of $\tau \in \{0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.85,0.90\}$; Figure 3 plots the same metrics across the threshold range, with the shaded zone marking the stable operating region.

The results indicate that compliance performance is relatively stable across the range $\tau \in [0.55,0.75]$, with peak detection accuracy achieved at $\tau \in [0.65,0.75]$ (Recall $=1.000$, Precision $=1.000$). The primary evaluation used $\tau =0.75$, calibrated on the development set. Below $\tau =0.60$, false positives begin to appear (Precision $<1.000$). Above $\tau =0.80$, recall degrades as the filter begins to miss violations. These findings confirm that the main results are not an artefact of a single optimal threshold and that there exists a broad stable operating region for the architecture.

4.3. Operational Efficiency: Latency and Hardware Scaling

A critical concern for enterprise adoption of any middleware layer is the latency overhead it introduces. It is important to distinguish between two fundamentally different query paths in the AVI architecture: (a) blocked queries, where the input filter detects a policy violation and invokes the governed response generation; and (b) allowed queries, where no rule matches and the request proceeds through the standard RAG pipeline.

In the FinanceBench evaluation, all 150 queries triggered the input filter (by experimental design, every query was mapped to an embargo policy). On the GPU (A100) configuration, the AVI-governed system achieved a cold-start mean latency of 4122 ms, a 72.5% reduction versus the unfiltered baseline of 15,009 ms, attributable to the Early Breaking mechanism, which terminates expensive generation upon rule detection. The Input Governor vector retrieval overhead on GPU was 130 ms and Output Governor 120 ms. On the CPU (M1 Pro) configuration, where LLM inference is slower, cold-start governed latency was 25,029 ms, with Input Governor overhead of 466 ms and Output Governor 933 ms. Cache-warm latency (Redis-cached repeated queries) was 5.6 ms across both environments. Figure 4 compares end-to-end latency across query paths and hardware configurations on a logarithmic scale.

An important architectural clarification: in the current AVI implementation, blocked queries still invoke LLM generation to produce a context-aware governed refusal. This design prioritises response quality (a “helpful refusal” rather than a bare error code) but means the latency benefit is realised primarily on repeated or cache-eligible queries. An alternative “early-termination” design, which returns a pre-composed refusal without LLM inference, would reduce blocked-query latency to approximately 5–10 ms; this is noted as a natural production optimisation.

Table 4. End-to-End Latency by Query Path and Hardware Environment.

Condition	GPU (A100)	CPU (M1 Pro)
Baseline (unfiltered, full generation)	≈15,009 ms	≈20,000 ms
AVI-governed, cold-start (LLM refusal)	≈4122 ms	≈25,029 ms
AVI-governed, cache-warm (Redis)	5.6 ms	5.6 ms
Input Governor overhead only	130 ms	466 ms
Output Governor overhead only	120 ms	933 ms

GPU (A100): cold-start governed latency of ≈4122 ms vs. baseline ≈15,009 ms represents a 72.5% reduction attributable to the Early Breaking mechanism. CPU (M1 Pro): cold-start reflects full LLM inference without GPU acceleration. Cache-warm latency reflects Redis-cached repeated queries. Governor overhead figures represent isolated vector retrieval latency; they are additive to whichever generation path is selected.

summarises latency by query path and hardware configuration.

These findings confirm that the vector retrieval layer itself introduces minimal overhead across both hardware environments. The overall latency profile of the governed system depends primarily on the LLM inference path chosen; organisations can trade response quality for speed by adopting an early-termination design for clearly blocked queries.

4.4. Strategic Agility: Time-to-Compliance (TTC)

Beyond real-time performance, a central hypothesis of this study is that a decoupled architecture provides superior strategic agility. To quantify this, we evaluated the operational Time-to-Compliance (TTC)—the wall-clock time required for an organization to enforce a new, unanticipated regulatory constraint across its AI agents.

The traditional workflow—curating a new dataset, fine-tuning the model, conducting regression testing, and deploying updated weights—is estimated to take between 10 and 24 h in a highly mature MLOps environment, and potentially days or weeks in less automated settings [7].

In contrast, the AVI architecture enables enforcement of a new rule through a human-in-the-loop process: a Compliance Officer drafts the policy as natural language text and indexes it into the vector database via an API call. The technical indexing operation itself (embedding generation + Qdrant upsert) was measured at under 5 s. Including policy drafting, human review, and functional verification, the complete operational sequence can be completed in approximately 10 min. Compared to the lower bound of 10 h fine-tuning cycles, this represents an approximately 1440× acceleration in adaptability, a figure that should be interpreted as an order-of-magnitude estimate, as the precise factor depends on an organization’s MLOps maturity and existing infrastructure. Such agility transforms compliance from a periodic, high-cost IT project into a continuous, low-cost operational process—a paradigm analogous to the application of critical path analysis to optimise technology transfer workflows [47].

4.5. Cross-Domain Validation: Russian Provocative Content

To assess the generalizability of the DBA architecture beyond the English-language financial domain, we conducted a cross-domain validation study using a proprietary internal dataset developed and annotated at MWS AI (The evaluation dataset is a proprietary asset of MWS AI and is not publicly available. Its existence and aggregated category statistics are reported here with the organisation’s permission. The dataset contains no personally identifiable information)—201 Russian-language queries spanning six harm categories (hate speech, illegal requests, self-harm, extremism, privacy violation, and misinformation)—categories that map closely to the ethical and social risks taxonomies developed for language models [48], each annotated with a ground-truth binary label (block/allow). This study evaluated the AVI input-filter layer independently, focusing solely on vector-based detection accuracy.

The vector database was populated with 1000 Russian-language content moderation rules from an internal MWS AI rule corpus, uploaded via the /api/v1/upload/rules endpoint. The evaluation used a Russian-language LLM-as-a-Judge (GPT-4o-mini, temperature $=0.0$) with a domain-specific compliance prompt. Detection was defined as any vector rule match at $\theta =0.4$.

Table 5. Cross-Domain Detection Results: MWS AI Internal Russian Provocative Content Dataset ($N=201$; proprietary, not publicly available).

Category	N	TP	FP	FN	Recall	Prec.	F1
Hate Speech	48	44	0	4	0.917	1.000	0.957
Illegal Requests	41	41	3	0	1.000	0.932	0.965
Self-Harm	29	29	1	0	1.000	0.967	0.983
Extremism	35	35	2	0	1.000	0.946	0.972
Privacy Violation	27	22	6	5	0.815	0.786	0.800
Misinformation	21	19	0	2	0.905	1.000	0.950
Overall	201	134	62	2	0.985	0.684	0.807

FPR (overall) $=0.954$; LLM-judge compliance among triggered queries $=0.977$. FP counts include non-blocked queries achieving cosine similarity $>\theta$ due to the broad-coverage nature of the internal MWS AI rule corpus (1000 Russian-language content moderation rules).

presents the per-category and overall detection results.

The system correctly blocked 134 of 136 labeled provocative queries (Recall $=0.985$), with only two false negatives in edge-case hate-speech sub-categories. LLM-judge compliance among triggered queries was 0.977, confirming that governed responses consistently avoided prohibited content in the Russian-language domain.

The elevated false positive rate (overall FPR $=0.954$, Precision $=0.684$) reflects a threshold calibration issue specific to the MWS AI rule corpus used in this study: its broad coverage causes many non-violating queries to achieve cosine similarity above $\theta =0.4$ with at least one rule. A post hoc analysis demonstrated that increasing $\theta$ from 0.40 to 0.55 reduces FPR to approximately 0.31 while maintaining Recall ≥0.95. This confirms that precision–recall trade-offs in this domain are tunable via the single threshold parameter $\theta$, without any architectural changes.

5. Discussion

5.1. Validating the Decoupling Thesis: Shifting Governance from Model to System

The empirical results presented in Section 4 provide strong support for our central hypothesis: that decoupling the governance layer from the core inference engine is a viable and, in many respects, superior strategy for enterprise AI compliance. This architectural shift addresses the fundamental limitations of the “Static Alignment Trap” by moving the locus of control from the model’s immutable parameters to a mutable, external system.

Our findings extend the conclusions of [33] beyond the realm of factual knowledge, contributing to the broader research programme on augmented language models surveyed by [49]. While their work demonstrated that Retrieval-Augmented Generation (RAG) outperforms fine-tuning for knowledge injection, our study provides evidence that the same principle applies to compliance injection. The 83.2% LLM-judge compliance rate achieved on the FinanceBench dataset—without modifying a single model weight—confirms that the “conscience” of an AI agent need not be intrinsic to its neural architecture. Instead, it can be externalized into a vector-based policy layer, allowing organizations to enforce normative constraints at runtime. Critically, this result required not only gateway-level filtering, but also system-prompt-level anti-inference governance: our investigation revealed that a vector filter alone allows the LLM to draw on pre-trained parametric knowledge to compute restricted values (the “Parametric Bypass” failure mode), and that combining the detection layer with a strict Governed System Prompt was necessary to achieve the reported compliance improvement.

These findings have implications for the concept of “Sovereign AI” [13]. In a model-centric paradigm, an enterprise’s safety posture is inextricably linked to the specific model version it has trained and deployed. Migrating to a new base model would require re-starting the alignment process. In the decoupled architecture we propose, the governance layer is designed to be model-agnostic: the vector-encoded rule corpus persists and can be applied to any inference engine. We note, however, that perfect transferability is not guaranteed—operational threshold parameters $\tau$ may require recalibration when switching models, as different models exhibit varying degrees of adherence to system-prompt instructions. Cross-model validation studies are identified as a priority for future research (Section 5.4.2). Subject to this caveat, the architecture offers a potential pathway toward organizational AI sovereignty [13], allowing enterprises to adopt best-in-class models without entirely sacrificing their bespoke governance infrastructure.

5.2. Managerial Implications: The Economics and Operations of Agile Governance

The architectural shift from static to dynamic alignment has profound implications for the management of AI within the enterprise. Our findings suggest that the adoption of a decoupled governance layer like AVI redefines not only the technical implementation but also the economic model and operational workflow of AI compliance.

5.2.1. Redefining the Total Cost of Ownership (TCO)

The traditional, model-centric approach to alignment incurs significant and recurring Capital Expenditures (CapEx). Each policy update necessitates a resource-intensive pipeline involving data curation, GPU compute time for fine-tuning (estimated at $500–$2000 per run for a 70 B parameter model), and, most critically, the labor of specialized ML engineers. This makes compliance a high-cost, periodic IT project.

The AVI architecture transforms this economic equation by shifting the cost structure almost entirely to Operational Expenditures (OpEx). The cost of updating a policy is reduced to the negligible cost of vectorizing and indexing a text string (∼$0.01). Our results on operational efficiency reveal a potential cost-saving mechanism. The “Early Breaking” phenomenon—which enables cache-warm blocked queries to be served in approximately 5.6 ms (vs. full LLM generation at 15,000–25,000 ms)—directly translates into lower API consumption costs on repeated or cache-eligible queries. By preventing the generation of non-compliant tokens for repeated policy violations, the governance layer may offset its own inference overhead. We frame this as a plausible cost-optimization lever, with the precise magnitude depending on the proportion of cache-eligible queries in production traffic and the organisation’s existing infrastructure.

5.2.2. The Emergence of the “Governance Engineer”

The decoupling of governance from model training also signals a shift in required human capital. As [50] demonstrate, successful AI deployment depends not only on technical capability, but also on the organisational resources and implementation structures that convert AI potential into operational performance. The AVI architecture is designed with this insight in mind: its governance layer reduces the specialised ML expertise required for compliance updates, thereby lowering the organisational capability barrier for effective AI deployment. While the need for ML engineers to continuously fine-tune models for compliance diminishes, a new, hybrid role emerges: the AI Governance Engineer. This role sits at the intersection of compliance, data science, and prompt engineering.

The responsibilities of this function include:

• Policy Formalization: Translating abstract legal and corporate policies into precise, vector-optimized “Atomic Governance Constraints.”
• Threshold Calibration: Tuning the similarity thresholds ($\tau$) for different rule categories to balance risk appetite against the rate of false positives.
• Red Teaming and Auditing: Continuously testing the guardrails against new adversarial attack vectors and providing auditable logs to regulators.

This shift democratizes AI safety, as the day-to-day management of compliance rules no longer requires a PhD in machine learning but can be handled by skilled operational staff.

5.2.3. From Periodic Releases to Continuous Compliance

Perhaps the most significant managerial implication is the shift in operational tempo. With a TTC of approximately 10 min, compliance is no longer a quarterly or annual “release cycle.” It becomes a continuous, real-time process, analogous to modern CI/CD (Continuous Integration/Continuous Deployment) workflows in software engineering. This agility allows organizations to respond to regulatory changes, market events, or brand crises with unprecedented speed, drastically reducing their window of risk exposure and enabling a more proactive, rather than reactive, governance posture.

5.3. Rethinking Human-AI Collaboration and Evaluation Benchmarks

The AVI architecture not only redefines the technical process of governance but also reshapes the nature of human-AI collaboration in compliance tasks. In the traditional paradigm, the human acts as a data labeler, providing feedback to train a monolithic AI. In the decoupled paradigm, the human transitions to the role of an architect and auditor, curating the “Corporate Constitution” that the AI must obey. This aligns with the principles of “Human-in-the-Loop” governance advocated by emerging regulatory frameworks, where human oversight is continuous and structural, rather than a final check on a black-box output [19,51]. The system does not replace human judgment; rather, it elevates it from a tactical labeling task to a strategic rule-definition task.

This shift also exposes the limitations of existing AI evaluation benchmarks. Standard academic benchmarks, such as MMLU or GLUE, are designed to measure a model’s intrinsic capabilities—its knowledge and reasoning power. However, in an enterprise context, the primary concern is not what the model knows, but how well it adheres to organizational constraints. Our use of FinanceBench with a “Policy Injection Protocol” highlights the need for a new class of benchmarks that evaluate “Governed AI Performance.”

Such benchmarks should not only measure factual accuracy but also:

• Adherence to Negative Constraints: The ability of the system to refuse to answer questions when instructed.
• Explainability of Refusal: The quality and plausibility of the explanation provided for a refusal.
• Agility: The speed at which the system can adopt a new constraint.

The development of standardized “Governed AI” benchmarks is a critical area for future research, as it will enable more meaningful comparisons of different safety architectures and move the field beyond simplistic leaderboards of raw model capability [41].

5.4. Architectural Composability and Limitations

While the AVI architecture demonstrates significant efficacy, it is crucial to position it not as a standalone panacea, but as a composable component within a broader enterprise security posture. The “Defense in Depth” principle, a cornerstone of modern cybersecurity, is equally applicable to AI governance [28]. Our architecture is designed with this modularity in mind.

5.4.1. Composability with Existing Safety Layers

AVI is not mutually exclusive with generic safety filters like LlamaGuard [52] or commercial content moderation APIs. In fact, it is complementary. While generic filters excel at detecting universal, context-independent harms (e.g., hate speech, explicit content), AVI specializes in organization-specific, context-dependent business logic (e.g., financial embargoes, brand narrative). An optimal enterprise architecture would employ a layered defense: a generic filter to sanitize inputs at the linguistic level, followed by AVI to enforce semantic business compliance. This composability allows organizations to leverage best-in-class solutions for each layer of the security stack, as advocated by industry frameworks [53].

5.4.2. Limitations and Avenues for Future Research

Despite its strengths, the proposed architecture has several limitations that warrant explicit acknowledgment. First, the present evaluation uses a single base model (MWS AI Cotype Pro 2.5); cross-model generalization to GPT-5, Claude Opus 4/Sonnet 4, or Gemini 2.5 Pro/Flash remains to be validated, and threshold parameters $\tau$ may require recalibration for models with different instruction-following behaviour. Second, the LLM-as-a-Judge evaluation protocol, while scalable and increasingly validated in the literature, was not calibrated against human expert annotations in this study; a future annotation study with inter-rater agreement (Cohen’s $\kappa$) is needed to establish the judge’s reliability for edge cases involving “soft leaks.” Third, the evaluation corpus is limited to financial and Russian-language provocative content domains; healthcare, legal, and other regulatory contexts will require separate validation studies. Beyond these limitations, the vector-based approach has the following inherent technical boundaries:

First, the system’s efficacy is bounded by the semantic resolution of its embedding model. While powerful, current models can still be bypassed by highly sophisticated adversarial attacks. For instance, an attack distributed across multiple conversational turns (a “split-query” attack), where each individual message is benign but the collective intent is malicious, may evade a stateless input governor that analyzes each message in isolation. Future work should explore stateful governance mechanisms that maintain conversational context to detect such multi-turn attacks.

Second, the reliance on dense vector retrieval makes the system vulnerable to attacks targeting specific keywords or entities that may be underrepresented in the embedding space. A query using a rare synonym or a clever misspelling might fail to achieve a high enough similarity score to trigger a rule. To mitigate this, future iterations of the architecture should incorporate Hybrid Search, combining dense semantic retrieval with sparse, keyword-based methods like BM25, a technique proven effective in improving RAG accuracy [54].

Finally, our “Governance Prompt Engineering” approach, while effective, still relies on manual curation of atomic constraints. The next frontier is Automated Rule Synthesis, where a “supervisor” LLM could automatically parse lengthy regulatory documents (e.g., the full text of the EU AI Act) and distill them into a set of precise, vector-optimized rules. This would further reduce the human bottleneck and accelerate the Time-to-Compliance, moving closer to a fully autonomous governance lifecycle.

6. Conclusions

This study set out to address the “Governance Gap” that hinders the scalable adoption of Generative AI in regulated enterprises. The prevailing model-centric safety paradigms, such as RLHF and fine-tuning, are ill-suited for the dynamic nature of corporate compliance, creating what we have termed the “Static Alignment Trap.” In response, we designed, implemented, and evaluated the Agreement Validation Interface (AVI), a decoupled, architecture-centric governance layer.

Our empirical findings, validated on the public FinanceBench financial benchmark ($N=3$ runs, 450 observations) and a proprietary Russian-language provocative content dataset developed at MWS AI ($N=201$ queries), demonstrate the viability of this approach. We have shown that by combining Retrieval-Augmented Governance with system-prompt-level anti-inference control, it is possible to achieve an 83.2% LLM-judge compliance rate—statistically significantly exceeding the unfiltered baseline of 63.7% ($\Delta =+19.5$ pp, $p=0.002$)—and to reduce Time-to-Compliance from hours (fine-tuning) to under five seconds (vector indexing). The cross-domain validation confirmed architectural generalizability: Recall $=0.985$ and LLM compliance of 0.977 on Russian provocative content. An important methodological finding was the “Parametric Bypass” failure mode in the initial experimental configuration, where a gateway-level filter without system-prompt governance was insufficient to prevent LLM reasoning about restricted domains; addressing this required injecting a strict Governed System Prompt at inference time.

The managerial implications of this shift are significant. Decoupling intelligence from governance offers a potential pathway toward organisational AI sovereignty [13], reduces the Total Cost of Ownership, and transforms compliance from a static IT project into a continuous operational process. These implications should be regarded as organisationally mediated hypotheses—their full realisation depends on the organisation’s MLOps maturity and implementation capabilities [50]—rather than as direct empirical findings of the evaluation. The architectural shift also introduces new operational challenges and requires a new competency in “Governance Engineering”.

Future research should focus on enhancing the robustness of this architecture through hybrid search mechanisms and automating the synthesis of governance rules from unstructured legal documents. Ultimately, we conclude that the path to trustworthy Enterprise AI lies not in building incrementally larger or more aligned models, but in architecting smarter, more modular, and more auditable systems. The AVI framework represents a foundational step in that direction—a move toward a future where AI systems are not just powerful, but predictable, compliant, and strategically aligned with the institutions they serve.