If a fully governed LLM security and financial audit were performed today, more than 70% of organizations would be classified as high risk.
Large language models (LLMs) are transforming the way businesses operate, but their rapid adoption is outpacing the implementation of proper controls and governance. The biggest challenge is that many organizations simply aren’t prepared for an audit. From unmonitored AI usage to the mishandling of sensitive data, gaps are everywhere.
Governance Gaps: No Rules, No Oversight
Most organizations have no formal governance structure for AI or LLM use. Employees often use AI tools without IT or compliance knowing, leading to inconsistent practices and potential breaches. Without governance, companies cannot demonstrate control over their AI processes, making them high-risk in an audit.
Some key problems include:
- Employees using LLMs to process sensitive or confidential information.
- Outputs being stored or shared without security checks.
- No designated team responsible for monitoring AI usage.
Sensitive Data Exposure: The Hidden Danger
LLMs are powerful, but they can retain or leak sensitive information if not handled correctly. Payroll, client lists, and financial data are particularly vulnerable. These exposures are not hypothetical—they are audit red flags that regulators and auditors take very seriously.
Examples of risky practices include:
- Copying and pasting internal documents into AI chat tools.
- Processing financial information through AI without encryption.
- Sharing outputs with external parties without proper safeguards.
The Shadow AI Problem
Even when organizations have some oversight, shadow AI undermines it. Shadow AI occurs when teams or individuals use AI tools outside official channels, leaving no trace for IT or compliance teams. This leads to:
- Untracked data flows that could expose sensitive information.
- Employees creating unofficial processes with unknown security risks.
- Auditors flagging these activities as critical control failures.
Policy Deficiency: No Guidelines, No Accountability
Another common failure point is the absence of documented LLM policies. Without clear guidelines:
- Employees don’t know what acceptable AI use is.
- Teams develop inconsistent or unsafe practices.
- Auditors cannot verify controls, even if risks are being mitigated informally.
Why This Matters
Failing an LLM security audit isn’t just about compliance. The consequences can include:
- Regulatory fines and penalties.
- Data breaches leading to financial loss and reputational damage.
- Legal exposure if confidential or client information is mishandled.
- Operational disruption as a result of untracked AI usage.
How NGA Helps
NGA has already implemented all the controls and safeguards organizations need to securely adopt and govern LLMs. Our platform is built with security at its core, giving you a trusted environment for AI without exposing your data to external systems.
With NGA, you get:
- Fully governed AI usage with built-in policy enforcement.
- Real-time detection and management of shadow AI across the business.
- Protection of sensitive financial, payroll, and client data through secure processing.
- Audit-ready compliance reporting for regulators and internal assurance.
Most importantly, NGA builds and deploys internal, secure LLMs that operate entirely within your environment. Your data never leaves your control, never trains external models, and is never exposed to third parties.
LLMs can transform your operations, and with NGA’s fully secured infrastructure, you can use them confidently without compromising regulatory requirements, financial integrity, or client trust.