Adam Kangiser, Iviry’s Compliance Analyst
AI Adoption Is Moving Faster Than Governance
Artificial intelligence is becoming part of everyday business operations. Employees use generative AI to summarize documents, draft communications, analyze data, write code, and accelerate research. Business units are embedding AI features into customer service, sales, operations, and decision support. Vendors are adding AI capabilities to platforms that organizations already use. The productivity opportunity is significant, but adoption is often happening faster than governance, security review, and risk management. That creates a familiar problem in a new form: the organization may be using technology it has not fully inventoried, understood, or controlled.
AI Risk Is Broader Than the Model
Many discussions about AI security focus narrowly on whether a model can be manipulated. That is important, but enterprise risk extends across the full system. What data is submitted to the AI service? Where is that data stored? Is it used to train a provider’s models? Which users can access the tool? Can the system connect to email, file storage, customer records, source code, or financial platforms? Can it take actions automatically? How are outputs reviewed? An AI application may be secure at the model layer while still creating risk through excessive permissions, weak integrations, poor data handling, or unmonitored use.
Shadow AI Creates Invisible Exposure
The first step toward secure AI adoption is visibility. Many organizations have a gap between approved AI use and actual AI use. Employees may create personal accounts, upload internal documents to public tools, or experiment with browser extensions and meeting assistants without realizing the implications. A blanket ban rarely solves the problem because employees will continue to seek productivity. A better approach combines clear policy, approved alternatives, technical controls, and practical education. The objective is to make secure behavior easier than insecure workarounds.
Treat AI as an Enterprise Risk
The NIST AI Risk Management Framework encourages organizations to govern, map, measure, and manage AI risk across the lifecycle. In practice, this means creating accountability before deployment. Each AI use case should have a business owner, a technical owner, a defined purpose, approved data categories, access boundaries, and a method for evaluating performance and harm. Higher-risk use cases should receive stronger review. An internal writing assistant is different from an AI system that recommends financial actions, makes employment decisions, handles controlled information, or changes production systems.
Secure the Data and the Connections
Data governance is central to AI security. Organizations should classify information and define what may be entered into different categories of AI tools. Sensitive data should be protected through approved platforms, encryption, access controls, retention limits, and contractual safeguards. Integrations deserve equal attention. Agentic AI systems can call APIs, retrieve files, send messages, create tickets, update records, and trigger workflows. Every connection expands the potential impact of a compromised account, malicious prompt, incorrect output, or poorly designed automation. Permissions should be narrow, actions should be logged, and high-impact steps should require human approval.
Design for Oversight and Failure
AI systems are probabilistic. They may generate incorrect information, follow ambiguous instructions, or behave differently when context changes. Security design must assume that errors will occur. Organizations should validate outputs for important decisions, test systems against misuse, monitor for unusual behavior, and preserve the ability to suspend automated actions. Human oversight should be meaningful rather than ceremonial. A reviewer needs enough context, authority, and time to challenge the output. Logging should show what information the system accessed, what instructions it received, what action it took, and who approved the result.
Build a Practical AI Security Program
A practical program does not begin with a massive committee or a complex control library. It begins with an AI inventory, an acceptable-use policy, approved tools, data handling rules, risk-tiered reviews, access controls, vendor due diligence, and monitoring. It should also include an incident process for AIrelated events, such as sensitive data exposure, unauthorized actions, model manipulation, or harmful output. As adoption grows, the program can mature through testing, metrics, and integration with broader cybersecurity and enterprise risk management.
The Iviry Perspective
AI can create real business value, but trust must be engineered into the way it is selected, connected, and operated. Iviry helps organizations align AI adoption with cybersecurity, identity, cloud, compliance, and operational controls. Secure AI is not about slowing innovation. It is about creating the conditions for innovation to scale without exposing the business to unnecessary risk.
