ESTABLISHING AN AI POLICY FRAMEWORK: BALANCING INNOVATION AND RISK

August 27, 2025

By Bryan Doherty

Artificial intelligence is already in your business. The question is whether it is happening by design or by accident. A clear AI policy gives your team a safe way to capture value while reducing risk. This article explains what generative AI is, why a policy matters, and a practical, step by step approach you can use to put guardrails in place. 


Generative AI refers to models that can create new content from prompts. The output can be text, images, audio, code, or structured data. These systems predict likely answers based on patterns learned from large datasets. In practice, this means tools that help write email, summarize documents, draft policies, generate images, and automate repetitive workflows. Generative AI can be highly useful when guided by clear instructions and reviewed by people. 


Employees in every organization are already experimenting with AI tools, but without clear guidance, businesses risk data leaks, inconsistent quality, and the rise of shadow IT. At the same time, regulations, contracts, and customer expectations are becoming stricter, making it essential to have documented controls that demonstrate responsible data handling. The good news is that the benefits of AI are real—boosting productivity, shortening cycle times, and allowing teams to focus on higher-value work. An AI policy transforms scattered, ad hoc experiments into structured, repeatable results that drive measurable impact. 


The first step is to set your intent and risk appetite. Clearly define what you want AI to achieve—such as improving customer response times, speeding up document drafting, or reducing manual data entry—and outline what you will not accept. Boundaries around confidentiality, safety, and brand protection should be established from the start. 


Next, define acceptable and unacceptable use. Approved use cases might include drafting internal communications, summarizing public articles, producing meeting notes from internal recordings, or generating first drafts of policies for review. At the same time, risky behaviors—like entering restricted data into public chatbots, using AI for final legal decisions, or publishing outputs without human oversight—should be explicitly prohibited. 


The third step is to choose tools with security in mind. Standardize on enterprise AI tools that support features like SSO, MFA, role-based access, logging, data residency options, and robust data retention controls. Before adoption, require vendor security reviews and signed data processing terms to ensure compliance and safeguard sensitive information. 


Then, address privacy and intellectual property. Clarify whether vendor models are trained on your data, establish clear data retention periods, and define ownership rights for AI-generated outputs. Employees should also be instructed to check for copyright concerns before publishing externally, or to rely on approved tools where these safeguards are already built in. 


Finally, keep a human in the loop. Require human review for any material impacting customers, legal agreements, finance, or HR. Wherever possible, define approval thresholds by risk tier, and incorporate accuracy checks and bias reviews into standard operating procedures. This ensures that AI remains a tool for efficiency without sacrificing accountability or quality. 




When creating an AI policy, there are common pitfalls to avoid. Try not making rules so strict that employees turn to unapproved tools. Be cautious of treating AI solely as a security concern rather than a business enabler. Never skip vendor due diligence and data retention reviews,  or allow production use without human oversight or an audit trail. You can adopt AI with speed and confidence by following a structured approach. This includes building an AI roadmap tied to business outcomes, selecting the right tools with proper vendor risk reviews, and ensuring secure implementation. It’s also important to formalize AI policies and standards, and to provide training for both leaders and staff to encourage safe and productive use. 



Uncomplicate IT News Blog

IMPACTFUL VIRUSES IN HISTORY: STUXNET

By Megan Poljacik • July 31, 2025
In the early 2000s, tensions in the middle east were on the rise. Iran’s government had begun expanding its uranium enrichment capabilities, insisting it was for nuclear energy purposes. However many countries around the globe feared it was a coverup for a nuclear weapons program. Despite pressure from United Nations and the International Atomic Energy Agency, Iran continued to enrich uranium. Frustrations reached a fever pitch and it appeared a conflict was imminent, until a mysterious solution came from a completely unexpected source: the Stuxnet Computer Worm.

DARK WEB MONITORING: ESSENTIAL SAFEGUARD OR WASTED SPEND?

By Megan Poljacik • June 26, 2025
With growing concerns about data breaches and stolen credentials, many businesses feel pressured to invest in services that claim to scan the dark web for leaked information. But before committing resources, it's worth asking whether these tools actually provide meaningful information or if they are an unproductive expenditure. The dark web is a hidden part of the internet that you can’t reach with standard browsers or search engines. It operates using a system called Tor, which stands for The Onion Router. Tor keeps users anonymous by sending their internet traffic through several different servers around the world, encrypting the data each time. This makes it extremely difficult to trace who someone is or where they’re connecting from. Unlike regular websites that end in .com or .org, dark web sites usually end in .onion and require the Tor browser to access them. While some people use the dark web for legitimate reasons like protecting their privacy or avoiding censorship, it’s mostly associated with hosting illegal activities like online black markets, where stolen corporate data is sold.

PENETRATION TESTING VS VULNERABILITY MANAGEMENT

By Megan Poljacik • May 29, 2025
When it comes to protecting your company’s infrastructure, two commonly used terms often cause confusion: vulnerability management and penetration testing. While they both serve the same purpose of keeping your data secure, they work very differently. Think of your company’s IT infrastructure like a museum that stores priceless artifacts. To keep it secure, you wouldn’t just install locks and hope for the best. You’d want to routinely check those locks, ensure windows are closed properly, and perhaps even hire someone to test how easy it is to break in. This is basically the difference between vulnerability management and penetration testing.