Category: blog
AI adoption is expanding across small and medium-sized businesses
Chatbots
Copilots
AI meeting tools
Transcription platforms
Code assistants
CRM automation
The security controls often do not expand with them
The main risks are consistent
- Unmanaged AI tools
- Sensitive data leakage
- Personal accounts
- Weak access controls
- Unreviewed AI features
- No monitoring or response process
NIST organizes AI risk management around four functions: Govern, Map, Measure, and Manage
CISA applies the same security principle to AI systems
For SMBs, the first step is identifying where AI is already being used
Mistake 1: AI tools are adopted without IT review
Employees often install or activate tools independently
Examples
- Public chatbots
- Browser extensions
- AI writing tools
- Meeting transcription services
- Free image generators
- Code assistants
- AI features inside CRM and collaboration platforms
- Personal accounts used for business work
These tools may process company information outside the approved technology environment
IT may not know
- Which tools are being used
- Which users have access
- What data is being processed
- Where data is stored
- How long data is retained
- Whether data is used for model training
- Which vendors have access
This is commonly called shadow AI
The issue is not AI use itself
The issue is untracked AI use
How to fix it
Create an AI tools inventory
For each tool record
- Vendor
- Business purpose
- Users
- Account type
- Data processed
- Retention terms
- Security controls
- Administrative owner
- Approval status
Classify every tool as
- Approved
- Conditional
- Prohibited
Approved tools should be available through company accounts
New tools should require a basic security review
A complete ban often drives usage underground
Approved alternatives reduce that risk
Mistake 2: Sensitive data is entered into public AI tools
Employees may paste information into an AI prompt because it produces a faster result
The information may include
- Customer names and contact records
- Financial statements
- Contracts
- Pricing information
- Employee records
- Health information
- Legal correspondence
- Internal procedures
- Source code
- Credentials
- Product plans
- Trade secrets
Once submitted, the business may lose control over the information
The data may be logged
It may be retained
It may be accessible to vendor personnel or connected services
The terms may differ between free and business plans
The same AI brand may provide different controls across account types
Do not assume that a familiar product provides business-grade data protection by default
How to fix it
Define prohibited data categories
The policy should state that confidential or regulated data cannot be submitted to public or personal AI accounts
Use data minimization
Remove
- Names
- Account numbers
- Email addresses
- Customer identifiers
- Internal project names
- Contract terms
- Unnecessary file content
Use approved enterprise tools when sensitive data must be processed
Verify
- Data isolation
- Retention settings
- Training policies
- Access controls
- Audit logging
- Vendor incident notification
- Contractual protections
A simple rule can be applied immediately
If the information would not be sent to an unknown third party by email, it should not be entered into an unapproved AI tool
Mistake 3: Personal AI accounts are used for company work
Personal accounts create several control gaps
- No central ownership
- No SSO
- No enforced MFA
- No offboarding process
- No company audit trail
- Unknown retention settings
- Unknown data ownership
- No administrative recovery
The business may also lose access to work product when an employee leaves
The account remains personal
The data remains outside company control
How to fix it
Require company-managed accounts for business AI use
Connect approved tools to the organization’s identity provider
Enforce
- SSO
- MFA
- Unique user accounts
- Role-based access
- Least privilege
- Central billing
- Offboarding procedures
- API key management
AI services should be included in employee onboarding and offboarding checklists
Shared credentials should not be used
API keys should be stored in a secrets manager
They should not be placed in source code, email, chat, or shared documents
Mistake 4: Embedded AI features are ignored
AI is not limited to standalone chatbots
It is being added to existing platforms
Examples include
- Email assistants
- Meeting summaries
- CRM recommendations
- Document analysis
- HR screening
- Customer service automation
- Cloud productivity copilots
- Security automation
- Recording and transcription tools
These features may be activated by administrators or users
They may process information already stored in the platform
The feature may create a new data flow without a separate procurement process
How to fix it
Review AI settings across all major business platforms
For each feature determine
- What information it can access
- Which users can activate it
- Where output is stored
- Who can view the output
- Whether prompts or files are retained
- Whether company data is used for training
- Whether the feature can trigger automated actions
Disable features that do not meet business requirements
Restrict access where full deployment is not appropriate
Require human approval before AI can send messages, modify records, approve transactions, or change system settings
Mistake 5: Human review is removed from business decisions
AI output can be inaccurate
It can produce false statements
It can omit important information
It can expose confidential content
It can make unsupported recommendations
The risk increases when output is sent directly to customers or used in financial, legal, HR, operational, or security decisions
How to fix it
Define review requirements
Human review should be required for
- Customer communications
- Contracts and proposals
- Financial analysis
- Compliance documents
- HR decisions
- Security alerts
- Public content
- Automated system changes
Reviewers should verify
- Facts
- Numbers
- Sources
- Permissions
- Confidentiality
- Business impact
Automation should begin in read-only mode where possible
Approval steps should be documented before write access is enabled
Mistake 6: AI security is separated from existing security
AI tools are connected to the same environment as email, identity systems, cloud storage, endpoints, and business applications
A compromised AI account can become an entry point
A malicious browser extension can read business content
A connected integration can expose files or records
A prompt injection attack can influence an AI-enabled workflow
AI security cannot be managed only through an acceptable-use document
It must be connected to existing controls
- Endpoint protection
- Identity management
- Network security
- Email security
- Backup
- Vendor risk management
- Incident response
- Security awareness training
X-Tek provides business IT support and managed support plans
AI use should be reviewed as part of the broader infrastructure
Mistake 7: No AI incident response process exists
Many businesses have incident response procedures for ransomware, phishing, and lost devices
AI-related events are not included
Potential scenarios
- A business account is compromised
- Sensitive data is entered into an unapproved tool
- An AI browser extension is found to be malicious
- A connected application exposes cloud files
- An AI workflow changes records incorrectly
- An employee uses a personal account for regulated data
- A vendor reports unauthorized access
How to fix it
Add AI scenarios to the incident response plan
The response should define when to
- Disable the account
- Revoke sessions
- Rotate API keys
- Remove integrations
- Preserve logs
- Contact the vendor
- Identify affected data
- Notify customers or regulators
- Review policy violations
- Restore affected systems
AI tool usage should be logged where supported
Monitoring should identify unusual access, unexpected data movement, and new integrations
Managed AI security from X-Tek
Managed AI security is a process
Not a single product
X-Tek can support SMBs with
- AI tool inventory
- Approved tool policies
- Data-handling rules
- Identity and access controls
- SSO and MFA configuration
- Vendor risk review
- SaaS AI feature review
- Endpoint and network monitoring
- Security awareness training
- Incident response planning
- Backup and recovery planning
- Ongoing security assessments
Our network security approach includes monitoring, threat detection, vendor evaluation, AI usage policy implementation, and infrastructure configuration
AI systems and connected business systems are monitored as part of the wider IT environment
Threats are identified and remediated through managed processes
Backup and recovery should also be reviewed
AI-related incidents can affect files, accounts, integrations, and business operations
X-Tek’s guidance on business continuity and backup limitations covers recovery planning, system redundancy, monitoring, and restoration testing
SMB AI security checklist
Review these items
- Is every AI tool inventoried
- Are personal AI accounts prohibited for company data
- Are approved tools documented
- Are sensitive data categories defined
- Is MFA enabled
- Are AI accounts included in offboarding
- Are embedded SaaS AI features reviewed
- Are API keys protected
- Is human review required for external output
- Are AI events included in incident response
- Are AI vendors evaluated
- Are logs available for investigation
- Are backups tested
- Is security monitoring active outside business hours
Any “no” identifies a control gap
The corrective action is usually clear
Inventory the tool
Restrict the data
Control the account
Monitor the activity
Document the response
AI adoption can continue
It must be managed within the business security program
Contact Information
Business Solutions Information Request:
https://xtekit.com/business-solutions-information-request/
815-516-8075