Your employees are already using AI. Some are pasting emails into chatbots, dropping meeting notes into LLMs for quick summaries, or using AI assistants to draft customer responses. Most aren’t thinking about what they’re sharing in the process.
43% of AI users have shared sensitive work information with AI tools without their employer’s knowledge (National Cybersecurity Alliance, 2025).
The risk isn’t malicious intent. It’s a habit. This article walks through AI prompt security best practices your Cleveland-area team can follow right now, before routine AI use becomes a routine data problem.
Key takeaways
- AI prompt security starts with clear rules about what employees should never enter into AI tools.
- Businesses should approve specific AI tools instead of letting employees choose their own.
- Sensitive data can be exposed through routine prompting, not just cyberattacks.
- Teams should start with low-risk use cases and require human review of all AI output.
- A proactive IT strategy helps Cleveland businesses use AI more securely and with confidence.
AI prompt security best practices for Cleveland-area teams
Use only approved AI tools
Decide which AI systems employees are permitted to use, and put it in writing. 78% of AI users bring their own AI tools to work, creating data exposure risk when companies lack approved platforms (Microsoft and LinkedIn, 2024).
Consumer tools such as free chatbots, personal OpenAI accounts, and unapproved extensions may store user data, pass user inputs through third-party APIs, and retain context as training data. Your IT team has no visibility into where company information goes when employees choose their own.
Approved options, such as Copilot for Microsoft 365 or GitHub Copilot, do not include enterprise-grade permissions, access controls, and audit logging. Define the approved list before it spreads further.
Create clear AI prompting rules
Good prompt engineering starts with knowing what to leave out. Spell out what sensitive data should never appear in user prompts: customer names, financial details, HR records, internal strategies, legal documents, and proprietary business information.
97% of organizations with AI-related breaches lacked proper AI access controls, underscoring the need for technical guardrails alongside written policy (IBM, July 2025).
Use secure prompt templates so employees don’t have to reinvent their own prompt structure each time. Templates reduce the surface area for data leakage. Pair the rules with access controls that limit which AI tools each role can access.
The OWASP Top 10 for LLM Applications lists sensitive information disclosure as one of the primary vulnerabilities in real-world LLM use. Addressing it through prompt engineering guardrails is the practical starting point.
Remove sensitive details before prompting
Before prompting any AI model, employees should anonymize or generalize inputs. Replace real customer names with placeholders. Use “a manufacturing client in Northeast Ohio” instead of the actual company name. Summarize internal meeting notes rather than pasting a full transcript as context.
This prompt engineering habit reduces data leakage even when employees use approved LLMs. Large language models process everything in user inputs, including context that employees may not think twice about. Sanitization before prompting is a repeatable practice any team member can build into daily workflows.
Start with low-risk use cases
Begin with use cases where mistakes have limited consequences: brainstorming, rewriting internal drafts, and summarizing non-sensitive notes. Avoid high-risk use cases, such as analyzing customer contracts or drafting HR decisions, until rules, training, and human review are fully in place.
AI-powered tools built for enterprise use include built-in safety functions and governance features that make them safer starting points than generative AI platforms designed for consumers.
Require human review
AI output should never be shared, saved, or acted on without a human-in-the-loop review step. The human element remains a factor in about 60% of breaches, underscoring the need for review and training to ensure safer AI use (Verizon DBIR, 2025).
A review step catches misinformation, confidentiality problems, and errors that model behavior doesn’t surface on its own. Treat AI as a drafting tool and automation assist, not a final authority. Build review into the workflow before distribution.
Train employees by role
AI security risks aren’t uniform. Sales, HR, finance, and operations teams face varying levels of prompt engineering exposure in their daily work. Role-based training with real examples is more useful than a generic policy document.
Walk through concrete scenarios: what does a risky prompt look like for an HR manager? Which inputs should a finance team avoid? Cover the most common IT security threats and AI-specific risks so employees understand the broader context in which AI systems operate.
What should never go into an AI prompt?
- Confidential internal information — strategic plans, leadership discussions, pricing details, internal reports. Even a helpful summary of past board meetings, pasted into a chatbot, creates exposure.
- Customer and client information — contracts, account details, support history, private communications. U.S. consumers reported losing more than $12.5 billion to fraud in 2024, reinforcing why sensitive personal and financial user data should stay out of prompts (FTC, March 2025).
- HR and employee data — compensation, performance notes, hiring records, personal details. GDPR and state-level privacy laws apply to AI processing of personal data, not just traditional storage.
- Legal and compliance-sensitive content — regulated records, attorney-client materials, compliance documentation. Prompt injection attacks and indirect prompt injection can cause LLMs to leak or misuse content in ways teams don’t anticipate at the time of input.
Why AI prompt security matters
More than 20% of U.S. firms expect to use AI in the first half of 2026, indicating a prompt need to scale with adoption (Federal Reserve, April 2026).
Most AI security discussions focus on external attacks: jailbreaks, prompt injection attacks, and adversarial inputs targeting AI models. For most Cleveland businesses, the more immediate risk is internal: employees using LLMs without thinking about what’s in the prompt.
Public LLM applications may store prompts as training data, pass inputs through external APIs, or lack the authentication your security teams need. When employees use these without guidance, the human behavior risk that drives most breaches has no guardrails.
How AI prompts security problems that usually happen
Copy-and-paste habits. Employees drop full emails, spreadsheets, or meeting notes into a chatbot for a faster answer. This is the most common way sensitive information moves outside an organization through AI.
Unapproved tool use
Teams adopt free or personal AI tools because they’re easy to access. IT has no visibility, no audit trail. Authentication controls and endpoint restrictions reduce this exposure.
Overtrusting output
Employees assume model responses are safe to share because they came from a tool that looks official. Output still needs review for confidentiality, accuracy, and compliance, regardless of the system.
Prompt injection and red teaming gaps
AI agents and LLM applications can be manipulated through malicious content embedded in documents or emails, a risk known as indirect prompt injection. Overriding previous instructions through crafted user inputs is one of the most documented vulnerabilities in the OWASP LLM framework.
Input validation and runtime sanitization in your AI pipelines reduce this exposure. Review implementations against OWASP guidelines and use red teaming frameworks available on GitHub to test prompt injection resilience before deployment.
Warning signs your business has an AI prompt security risk
If any of these apply, address them before exposure grows:
- Employees are already using AI tools with no written policy governing prompts.
- Teams are using different LLMs without central approval or visibility.
- No one has defined what data can and cannot be included in a prompt.
- There are no secure prompt templates or guidelines for prompt engineering.
- Your IT team has no incident response plan for AI-related data exposure.
Help your Cleveland team prompt AI more safely
The productivity case for AI is real. So is the prompt a security risk?
The right response isn’t to block AI tools. It’s to approve the right ones, build clear prompt engineering rules, train employees by role, and require human review before any output leaves the team.
Keystone Technology Consultants has helped Akron and Cleveland-area businesses put the right cybersecurity controls in place for more than 25 years.
If your team is already using AI without clear rules, you’re exposed.
Build a secure AI prompt framework with Keystone and get expert on-site support within 60 minutes, no long-term contracts.
FAQs
What should employees never put into an AI prompt?
Employees should never enter confidential business information, customer details, HR records, financial data, legal documents, or passwords into prompts, including consumer-grade LLMs and free chatbots. Even general-sounding inputs expose sensitive information when full emails or meeting notes are pasted in as context. When in doubt, leave the data out.
Are free public AI tools safe for business use?
Not always. Free public LLM applications may store user inputs as training data, pass prompts through third-party APIs, and lack the authentication and audit logging enterprise environments require. Businesses that don’t approve specific tools first have no visibility into where company data is going.
How can Cleveland-area teams use AI more securely?
Start with approved tools and a documented IT security policy that covers AI use. Limit prompting to lower-risk use cases, train employees by role, and require human review before any output is shared. That’s how teams get the speed benefits of AI without the data leakage that comes from unmanaged prompting habits.
