Every conversation about industrial AI should include a cybersecurity conversation. Often it doesn’t.

AI systems connect to operational technology networks. They consume production data. Some of them influence or control equipment. That creates security implications that manufacturers need to understand.

I’m not a cybersecurity specialist, but I’ve watched enough AI implementations encounter security challenges to offer practical guidance.

Why industrial AI creates security considerations

Connectivity between IT and OT

Traditionally, operational technology (OT) networks—the systems running your factory—were isolated from IT networks and the internet.

AI changes this. AI systems typically live in IT environments (or cloud) but need data from OT systems. This creates connections that didn’t exist before.

Every connection is a potential attack path.

Cloud exposure

Many AI solutions run in cloud platforms. Production data leaving your premises raises questions:

• Who can access that data?
• How is it protected in transit and at rest?
• What happens if the cloud service is compromised?
• What are the legal implications for data sovereignty?

New attack surfaces

AI systems themselves can be attack targets:

• Poisoned training data could cause models to make wrong decisions
• Adversarial inputs could fool vision systems or other AI sensors
• Compromised AI recommendations could manipulate operations

These attacks are still relatively theoretical in industrial contexts, but security-conscious organisations consider them.

Increased complexity

More systems, more integrations, more potential vulnerabilities. Every component added to your environment increases the attack surface.

Australian regulatory context

Several regulatory developments affect industrial cybersecurity:

Critical Infrastructure legislation: The Security of Critical Infrastructure Act 2018 (as amended) imposes cybersecurity obligations on certain sectors including energy, water, and communications. Manufacturing isn’t directly covered, but supply chain relationships may bring you in scope.

Privacy Act: Personal data about employees or customers processed by AI systems must be protected under privacy obligations.

Industry standards: Many sectors have specific cybersecurity standards. Food safety, pharmaceutical, automotive—various frameworks apply.

Contractual requirements: Major customers increasingly require cybersecurity certifications or compliance with specific standards.

Even without direct regulatory mandates, demonstrating cybersecurity competence is increasingly a business requirement.

Practical security considerations for industrial AI

Network architecture

Segment your networks: Production networks should be separate from IT networks, with controlled interfaces between them.

Use DMZ architectures: Data flowing from OT to IT/cloud should pass through demilitarized zones with appropriate filtering and monitoring.

Limit connectivity: Only connect what needs to be connected. Every integration point is a potential vulnerability.

Control data flow direction: Ideally, data flows one way—from OT to IT/analytics. Be very careful about anything that writes back to production systems.

Access control

Least privilege: AI systems should only access the data and systems they need. Broad access creates unnecessary risk.

Strong authentication: Multi-factor authentication for AI system administration. Secure API keys and credentials.

Audit logging: Track who accesses AI systems and data. Monitor for anomalies.

Vendor access: If vendors need access for support, manage this carefully. Time-limited access, monitoring, clear agreements.

Data protection

Encryption in transit: Data moving between systems should be encrypted.

Encryption at rest: Sensitive data stored by AI systems should be encrypted.

Data minimisation: Collect only what you need. Delete what you don’t need to retain.

Classification: Understand what data is sensitive. Apply appropriate protection levels.

Cloud security

Assess provider security: Understand your cloud provider’s security controls. Major platforms (AWS, Azure, GCP) have extensive security, but you need to configure it correctly.

Data location: Know where your data is stored. Consider data sovereignty implications.

Contractual protections: Review service agreements for security commitments.

Exit strategy: How would you extract your data if you needed to leave the provider?

Vendor security assessment

Security questionnaires: Ask AI vendors about their security practices, certifications, and incident history.

Penetration testing: Has the vendor’s system been independently tested?

SOC 2 or similar: Does the vendor have security certifications?

Contractual protections: What does the contract say about security obligations, breach notification, and liability?

Incident response

Prepare for incidents: AI systems will have security issues at some point. Have a plan.

Monitoring and detection: Can you detect if an AI system is compromised or behaving abnormally?

Containment: Can you quickly isolate a compromised AI system without major operational impact?

Recovery: How would you restore normal operations after an incident?

AI-specific security considerations

Model integrity

Can you verify that AI models haven’t been tampered with? For critical applications, model integrity matters.

Input validation

AI systems should validate inputs. Anomalous data should trigger alerts, not just be processed.

Adversarial robustness

For vision systems and other AI that processes real-world inputs, consider whether attackers could fool the system with crafted inputs.

Training data protection

The data used to train AI models can be sensitive. Protect it accordingly.

Explainability for auditing

When investigating security incidents or auditing AI behavior, can you understand what the AI did and why?

Working with security teams

If your organisation has security staff, involve them early in AI projects.

Architecture review: Security should review how AI systems will connect to production environments.

Vendor assessment: Security can help evaluate AI vendor security practices.

Risk assessment: Identify and document security risks and mitigations.

Ongoing monitoring: Include AI systems in security monitoring programs.

If you don’t have dedicated security staff, consider engaging external security consultants for significant AI implementations.

Finding the right balance

Security can slow things down. Every control adds friction. Requirements for security review can delay projects.

But the alternative—implementing AI with major security gaps—creates unacceptable risk for critical operations.

The goal is proportionate security. More controls for higher-risk implementations (AI that affects production, handles sensitive data, or connects to critical systems). Simpler approaches for lower-risk experiments.

Defining “proportionate” requires understanding both the security risks and the operational context. This is where manufacturing experience and security expertise need to work together.

Getting security right

A few recommendations:

Include security from the start: Don’t bolt security on after the fact. Consider it in AI system design and vendor selection.

Educate your team: Make sure people implementing AI understand basic security principles. They don’t need to be experts, but they should know what questions to ask.

Learn from incidents: When security issues occur (and they will), learn from them. What happened? How can you prevent similar issues?

Stay current: Threats evolve. Keep your security practices current with emerging risks.

Where to get help

Industrial cybersecurity is a specialised field. Resources include:

Australian Cyber Security Centre (ACSC): Government guidance and alerts.

Industry bodies: Sector-specific cybersecurity groups and guidance.

Specialist consultants: Firms focused on industrial/OT cybersecurity.

AI implementation partners: When working with AI consultants Brisbane or similar firms, ensure they consider security in their implementation approaches.

The bottom line

AI in factories creates real value. But it also creates security considerations that manufacturers can’t ignore.

Take security seriously. Include it in planning. Get appropriate expertise. But don’t let security paralysis prevent you from capturing AI’s benefits.

The goal is secure AI implementation—getting the value while managing the risks appropriately.

Manufacturers who get this balance right will adopt AI confidently. Those who ignore security create real operational risk. Those who use security as an excuse to avoid AI entirely fall behind.

Find the middle path. Your operations—and your business—depend on it.

Working with Team400 or similar experienced partners can help you navigate both the AI implementation and the security considerations that come with it.