Securing Your AI Implementations: Practical Guidance

AI adoption is accelerating. Most businesses are now using AI in some form - ChatGPT for content, Copilot for productivity, AI features in business software.

This creates new security considerations. Here’s how to adopt AI securely.

The Security Questions You Should Be Asking

Before adopting any AI tool:

Data handling:

  • What data will this AI process?
  • Where is the data processed and stored?
  • Will our data be used to train AI models?
  • How is our data protected?

Access control:

  • Who can use this AI tool?
  • What permissions does the AI have?
  • How is access authenticated?

Output handling:

  • How will AI outputs be used?
  • Who verifies AI-generated content?
  • What happens if AI outputs are wrong?

Vendor security:

  • What’s the vendor’s security posture?
  • What certifications do they hold?
  • What’s their incident response process?

Categories of AI Risk

Data leakage: Sensitive information entered into AI systems may be processed in ways you don’t control. Customer data, proprietary information, personal details - all could be exposed.

Inaccurate outputs: AI makes mistakes. If decisions are based on AI outputs without verification, mistakes can propagate.

Prompt injection: Malicious inputs can manipulate AI behaviour. In AI systems that process external content (emails, documents, websites), this is a real concern.

Shadow AI: Employees using unauthorised AI tools, often consumer-grade services, without IT knowledge. Data leakage risk without visibility.

Dependency risk: Over-reliance on AI for critical functions without fallback processes.

Tier 1: Consumer AI Tools (ChatGPT, Claude, Gemini)

The AI tools most SMBs start with.

Security considerations:

Data usage: Free tiers often use your inputs for training. Enterprise tiers typically don’t, but check.

Data handling: Data is processed on vendor infrastructure. Understand retention policies.

Privacy: Personal data entered may create Privacy Act obligations.

Best practices:

  1. Use enterprise tiers for business use. Better data protection.

  2. Establish usage policies. What can and can’t be entered.

  3. Don’t enter sensitive data. Customer PII, financial data, proprietary secrets - keep them out.

  4. Verify outputs. Don’t publish or act on AI content without review.

  5. Consider Australian data residency where important.

Tier 2: Integrated AI (Microsoft Copilot, Google Duet AI)

AI built into your existing productivity platforms.

Security considerations:

Data access: These tools access your business data. They can see what you can see.

Data protection: Typically better than consumer tools. Microsoft and Google have enterprise data protection commitments.

Permission risks: If permissions are over-broad, AI can surface data users shouldn’t see.

Best practices:

  1. Review permissions. Before enabling Copilot, ensure your M365 permissions are correct. AI will surface what it can access.

  2. Use data classification. Label sensitive data. Configure AI to respect labels.

  3. Enable appropriate controls. Microsoft Purview, Google data loss prevention.

  4. Train users. Understand what AI can access and appropriate use.

  5. Monitor usage. Audit logs show AI activity.

Tier 3: Custom AI Implementations

Building AI into your own applications or processes.

Security considerations:

Model security: AI models can be attacked, manipulated, or stolen.

Training data: What data trains your models? Is it appropriate? Secure?

Integration risks: How does AI connect to other systems? What can it access?

Output validation: How are AI decisions validated before action?

Best practices:

  1. Security by design. Build security into AI projects from the start.

  2. Input validation. Protect against prompt injection.

  3. Output verification. Human review for significant decisions.

  4. Access control. Limit what AI can access and do.

  5. Monitoring. Log AI activity for audit and incident response.

For custom AI work, engaging specialists like Team400 ensures security is built in from the start rather than bolted on later.

Data Classification for AI

Not all data should be treated the same.

Categories to consider:

Unrestricted: Public information. Safe for any AI tool.

Internal: Business information not for public release. Enterprise AI tools with appropriate controls.

Confidential: Sensitive business data. Careful consideration before AI use. Strong controls required.

Restricted: Customer PII, financial data, health information. Most restrictive AI policies. May be prohibited entirely.

Practical implementation:

Create a simple AI data policy:

  • What can go into consumer AI (not much)
  • What can go into enterprise AI (more, with controls)
  • What should never go into AI (critical data)

Train employees on the categories.

The Shadow AI Problem

Employees are using AI whether you like it or not.

The risk:

Employees use free AI tools for productivity. They paste in customer emails, financial data, proprietary information. No oversight. No controls.

The solution:

  1. Provide approved alternatives. If you don’t give people AI tools, they’ll find their own.

  2. Create clear policies. What’s allowed, what isn’t.

  3. Educate on risks. Explain why controls matter.

  4. Monitor for shadow AI. Network monitoring, endpoint controls.

The balance:

Too restrictive and people work around you. Too permissive and data leaks. Find the middle ground.

Vendor Assessment for AI

When evaluating AI vendors:

Security questions:

  • How is our data protected?
  • Is our data used for training?
  • Where is processing performed?
  • What security certifications do you hold?
  • How do you handle security incidents?
  • What access controls are available?
  • How is data retained and deleted?

Red flags:

  • Vague answers about data handling
  • No security certifications
  • Unclear data residency
  • No options to opt out of training
  • Inadequate access controls

Good signs:

  • Clear, specific data handling commitments
  • Relevant certifications (SOC 2, ISO 27001)
  • Enterprise options with better controls
  • Transparent about limitations

AI and Privacy Act Compliance

If AI processes personal information:

Considerations:

  • Collection consent may not cover AI processing
  • Cross-border data transfer rules apply
  • Data minimisation principles apply
  • Purpose limitation may be affected

Practical steps:

  • Review privacy policies for AI use
  • Assess whether AI processing is covered by existing consent
  • Consider Privacy Impact Assessments for significant AI use
  • Consult legal counsel for complex situations

Building an AI Governance Framework

For businesses getting serious about AI:

Elements:

Policy:

  • Approved AI tools and services
  • Data handling requirements
  • Use case guidelines
  • Prohibited uses

Process:

  • AI tool approval process
  • Vendor assessment requirements
  • Implementation review
  • Incident response for AI issues

Monitoring:

  • AI usage visibility
  • Audit logging
  • Compliance checking

Training:

  • Awareness of AI risks
  • Appropriate use guidance
  • Verification practices

Start simple. Evolve as AI use matures.

Getting Help

AI security is a developing field. Working with AI consultants Brisbane or similar specialists can help:

  • Assess current AI usage and risks
  • Develop appropriate policies
  • Implement secure AI solutions
  • Establish governance frameworks

The intersection of AI expertise and security expertise is particularly valuable as businesses navigate this space.

Practical Checklist

For SMBs starting to think about AI security:

Immediate:

  • Inventory current AI tool usage
  • Review terms of service for AI tools
  • Establish basic data handling expectations
  • Communicate expectations to staff

Short-term:

  • Develop formal AI usage policy
  • Assess enterprise AI options
  • Include AI in security awareness training
  • Review vendor security for key AI services

Ongoing:

  • Monitor AI usage
  • Update policies as landscape evolves
  • Stay current with regulatory developments
  • Adjust as AI capabilities change

The Balance

AI brings genuine productivity benefits. Security concerns shouldn’t prevent adoption - they should guide it.

The goal:

Adopt AI in ways that capture value while managing risk appropriately.

The approach:

  • Understand what data is involved
  • Choose appropriate tools for sensitivity levels
  • Implement proportionate controls
  • Verify important outputs
  • Monitor for problems

This is achievable for any business willing to think it through.

Final Thought

AI is becoming embedded in how business operates. Security must evolve to address AI-specific risks while enabling AI’s benefits.

The businesses that approach AI thoughtfully - understanding risks, implementing appropriate controls, maintaining human oversight - will get the benefits without the incidents.

Working with specialists like AI consultants Sydney can help navigate this evolving landscape. But the fundamentals are accessible: know what AI you’re using, control what data goes into it, verify what comes out.

That’s a solid foundation for secure AI adoption.