Shadow AI vs Approved AI in Financial Institutions

Gen AI adoption among employees has shot up significantly in the last two years. Almost every knowledge worker uses Gen AI tools for tasks like data analysis or text summarization — these are some of the most basic use cases. 

It goes much deeper, where proprietary information or sensitive customer data can be used. Is there any harm in using such tools and sharing important organizational data? For this, we need to understand the concept of Shadow AI and Approved AI. 

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What is Shadow AI and Approved AI? 

Shadow AI refers to the use of AI tools or models by employees or departments without the institution’s authorization. Such shadow AI often arises when staff find official solutions lacking or too slow and turn to external AI to boost productivity. 

It’s akin to using any software or apps without the permission of the IT department. However, because these tools operate outside governance, they can expose the organization to hidden risks in security, compliance, and reliability. 

Approved AI, by contrast, denotes AI systems and tools that are sanctioned by the IT department and adhere to the financial institution’s policies. These are models or applications that have passed the organization’s vetting process. This AI is integrated into the bank’s infrastructure with the knowledge of IT and risk management teams, ensuring proper controls, documentation, and monitoring. 

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Examples of Shadow AI Use in Financial Institutions (FIs)

As mentioned earlier, using Gen AI tools for text summarization are some of the most basic use cases. In financial institutions, where protecting proprietary information and customer data is crucial, turning to unsanctioned AI tools across functions can be dangerous.  Here are a few examples of Shadow AI use in FIs: 

Risk Assessment

Analysts might quietly use a generative AI tool to summarize risk reports or run quick predictive analyses. This means exposing sensitive financial data to generate investment summaries. Such usage is illustrative of shadow AI. It’s done for efficiency, but without clearance, and potentially compromising data privacy.
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Customer Service & Communication

In customer-facing roles, staff may leverage AI chatbots or text generators to draft responses, product explanations, or marketing content. A sales team using a Gen AI tool to draft client proposals is one example: initially it might be to save time on writing, but over time they could end up pasting in pricing strategies or internal metrics to get better outputs. 
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Fraud Detection & Operations

Shadow AI can appear when tech-savvy employees build unofficial models or use open-source AI for tasks like fraud pattern detection or credit scoring. For example, a data scientist might deploy an open-source anomaly detection model on transaction data to flag fraud faster, without involving the IT department or model risk management. While well-intentioned, such a rogue solution is not vetted for accuracy or security. 

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These examples underscore a growing trend: employees often circumvent official policy to tap AI’s power. Even when banks explicitly ban certain AI (like public chatbots), employees find workarounds. This shadow AI adoption is widespread, cutting across roles from software developers to business analysts. The result is an “invisible” layer of AI in the enterprise, which leadership might not even realize is running day-to-day operations. 

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Risks and Downsides of Shadow AI in Financial Services

For financial institutions that thrive on trust, compliance, and security, the risks of unapproved AI usage are significant:

Data Breaches

Unvetted AI tools may store or transmit sensitive data externally. Employees using unsanctioned AI often input confidential information (customer financials, PII, etc.) into these tools. Without proper safeguards, this data could be exposed. 

A recent poll of CISOs found that 1 in 5 companies experienced data leakage due to employees using generative AI. In finance, such a leak of client data or trading strategies can violate privacy laws and increase distrust.
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Regulatory Non-Compliance

Shadow AI tools bypass the usual compliance checks. This can lead to inadvertent violations. For example, uploading EU customer data to an AI service could flout GDPR rules. 

Moreover, models that drive decisions (credit underwriting, trading algorithms) must typically be validated and documented per regulatory guidance; a shadow AI model used in such a process would not meet those standards, leaving the bank exposed in audits and severe imminent penalties.
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Lack of Auditability and Control

Because IT and risk teams are unaware of shadow AI deployments, there is no audit trail or formal monitoring of these tools. Decisions made or content generated by an unauthorized AI cannot be easily reproduced or explained. 

This opacity is dangerous in finance. Think of a rogue AI making a lending decision that turns out biased or erroneous. The bank would struggle to explain the rationale to regulators or customers. 
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Unintended or Biased Outcomes

AI models not vetted by the organization might produce inaccurate, inappropriate, or biased results. In financial contexts, this is especially problematic – e.g., an unofficial AI tool used for credit risk assessment could inadvertently incorporate bias against a demographic, leading to discriminatory outcomes. 

Because shadow AI operates outside the bank’s model risk management, such issues may not be caught until damage is done. The lack of oversight means outputs might not align with company guidelines or values, causing reputational harm if, say, an AI chatbot gives an offensive or misleading answer to a customer. Poor decisions driven by unchecked AI can translate into financial loss and brand damage.
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Reputational Damage

If it comes to light that a bank’s employees misused AI or leaked data through an AI tool, the reputation fallout can be severe. Customers and regulators expect banks to safeguard information diligently. Publicized incidents, like the example of a bank’s staff using a Gen AI tool with client data, can lead to headlines about the bank “not in control” of its AI usage. 

This undermines customer trust and invites regulatory scrutiny. The reputational hit often far exceeds the immediate operational impact of the incident. In a sector built on credibility, the mere perception of careless AI use can be devastating.

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From Shadow to Sanctioned: Transitioning to Approved AI

For financial institutions, the solution is not to reject AI, but to bring it into the fold under proper governance. Leaders in banking are now focusing on how to transition from the chaos of shadow AI to a state of approved, well-managed AI use. Key steps for this transition include: 

Set Up Policies Governing AI Usage

A framework outlining AI usage can ensure its responsible usage. Such a framework positions an organization as a forward thinking institution, clear ownership and oversight for AI activities exist. It is even equally important to create a Gen AI role framework, where responsibilities are assigned to employees for deploying, governance, ethics, and more. 

The policies are meant to define what is allowed, prohibited, and requires approval. These should align with the financial institution’s risk appetite and operations standards. The guidelines will establish a code of conduct for each department; for instance, the risk and compliance teams may need more stringent policies, while the marketing team has some freedom.  
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Ensure Regulatory Compliance and Ethical Use

Approved AI must be deployed in line with all applicable regulations and internal ethics policies. Banks should map out which regulations (GDPR, OCC guidelines, CFPB expectations, etc.) apply to each AI use case. Controls need to be implemented to manage AI-specific risks – for instance, testing models for bias and fairness before use, and instituting human-in-the-loop checkpoints for high-stakes decisions. 

Clear ethical AI guidelines (covering principles like fairness, accountability, and transparency) should be documented and enforced. By proactively working within regulatory frameworks – even engaging regulators with transparency about AI projects – institutions can avoid compliance surprises and use AI as a trustworthy tool.
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Maintain an AI Inventory and Monitoring

An effective practice is to inventory all AI models and tools in use. Just as IT maintains an asset register, risk management should maintain a system inventory map for AI (including third-party AI services). For each AI application, document who owns it, what data it uses, and its validation or approval status. 

Regular risk assessments should be conducted on these tools. Equally important, monitor for any shadow AI usage that still occurs: using Data Loss Prevention (DLP) systems and network monitoring to detect unauthorized AI-related traffic. If employees are found adopting new tools, investigate why – it might indicate a legitimate business need that an approved solution could address. The goal is centralized visibility: you cannot protect data or integrity if you don’t know where an AI tool is operating.
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Strengthen Security and Access Controls

As AI is integrated officially, ensure that it’s done securely. Approved AI platforms should be deployed in secure environments (on-premises servers or vetted cloud environments) with proper access controls, encryption, and audit logging. Limit who can input or retrieve sensitive data via AI – for instance, using role-based access so only authorized personnel use a customer data analysis AI. 

All AI outputs and decisions should be logged for auditability. By building AI into the enterprise security architecture (and, for example, keeping AI within the firewall whenever possible), banks can prevent the types of data leakage that plague shadow AI. Additionally, ensure third-party AI vendors comply with the bank’s security requirements – include AI usage clauses in vendor risk assessments.
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Employee Training and Cultural Change

Technology solutions won’t succeed without user buy-in. It’s crucial to cultivate a culture where employees understand why certain AI tools must be approved and the risks of going rogue. Conduct regular training and awareness programs about the dangers of shadow AI (data leaks, penalties, etc.) and the proper channels to request new AI capabilities. 

Encourage employees to come forward with any new AI tool they wish to try, in a “ask before you act” approach – and make it non-punitive, focusing on improvement rather than blame. When staff see that leadership supports innovation within guardrails, they are less likely to hide their AI experiments. 

Some banks, for example, have chosen not to blanket-ban tools like ChatGPT, but rather to provide internal alternatives and clear guidelines, so that employees don’t feel the need to circumvent policies. Ultimately, an informed and vigilant workforce is the best defense against shadow AI creeping back in.

By taking these steps, financial institutions can migrate from an ad-hoc, risky AI landscape to a controlled environment where AI’s benefits can be reaped safely. The end-state is “Approved AI” use: where AI is a strategic asset deployed with full visibility, proper controls, and alignment to the bank’s objectives and obligations.

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Weave by Arya.ai: Enabling Enterprise-Grade, Approved AI

To effectively harness AI under governance, banks must turn to specialized platforms that provide built-in compliance and security features. One such solution is Weave by Arya.ai, an enterprise-grade AI orchestration platform designed for financial institutions. Weave helps leadership teams deploy approved AI with robust guardrails so they can innovate confidently.

Unified AI Orchestration with Compliance

Weave is an AI agent orchestration platform that bridges the gap between powerful GenAI models and the bank’s enterprise applications. Crucially, it is built from the ground up to ensure compliance, security, and performance at scale. The platform allows banks to empower their teams with AI capabilities while centrally managing and monitoring those AI “agents” to guarantee they operate within the institution’s rules. 
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Secure, On-Premise or Hybrid Deployment

Recognizing banks’ data sensitivity, Weave can be deployed in a way that keeps data within the bank’s control. This addresses one of the biggest shadow AI worries: with Weave, employees can use AI without copying data into unknown external services. All AI processing can occur in a contained, audit-ready environment. 
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Pre-Built AI Models and Modular Integration

Arya.ai’s Weave comes with a library of 100+ enterprise-ready AI models and integrations, tailored for industries like finance. These include models for tasks such as biometric liveness detection, document processing, PII data masking, fraud anomaly detection, and more – all readily available within the platform. 

By tapping into this library, employees don’t need to seek unapproved tools for common tasks; they have vetted models at their fingertips. Weave also integrates seamlessly with existing systems (databases, CRMs, core banking systems, etc.), with over 100 connectors available. 

This ensures AI projects have real-time access to internal data without exposing that data externally, and it provides centralized governance across all AI pipelines. In essence, Weave plugs AI into the enterprise workflow in a governed manner, instead of employees resorting to off-the-grid workarounds.
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Flexible Model Management and Control 

The platform supports “model pivoting” and multi-model orchestration, meaning teams can use the right AI model for the right task and switch as needed. All these AI agents (whether a large language model for a chatbot or a vision model for document checks) are managed through Weave’s standardized architecture. 

Each AI agent runs with strict permissions and context – Weave’s design allows AI to fetch the data it needs without ever exposing raw data or exceeding its access rights. This containment is a critical guardrail: even powerful AI models are sandboxed so they only see and do what they are permitted, preventing the kind of uncontrolled data usage typical of shadow AI.
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Built-In Guardrails

The platform’s security framework means each agent operates under clear boundaries and permissions, and can be shut down or adjusted centrally if needed. For high-stakes use cases, Weave can incorporate human-in-the-loop checkpoints or approval workflows. 

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Conclusion 

Shadow AI highlights the immense hunger among employees for becoming more productive. What if we provide employees with an approved system where the customer and organizational data isn’t at stake. 

Approved AI eliminates the need for rogue usage of unvetted platforms, where FIs can turn a chaotic situation into a strategic advantage. 

The path forward for leadership is clear – embrace AI’s potential, but do it with eyes open, under the right oversight. If you’d like to deploy such an AI in your organization, connect with our experts here.