How Data Security and Governance Drive AI Model Choices in Manufacturing: Protecting IP, Preventing Threats, and Building Trust
Your data is your most valuable asset, and protecting it is now the deciding factor in AI adoption. Learn why security is shaping every AI decision, how leaders are responding, and what practical steps you can take to safeguard your future.
The manufacturers who succeed with AI will be those who treat security as the foundation, not the afterthought. This is where governance, trust, and resilience become the real ROI drivers. You’ll discover how to evaluate providers, what risks to anticipate, and how to build safeguards that protect both your IP and your competitive edge.
Background: The second-annual ROI of AI in manufacturing report, commissioned by Google Cloud and conducted by National Research Group.
Data security and governance are fueling AI model provider choices.
For manufacturers, maintaining the highest standards of security is crucial for safeguarding invaluable IP and assets across every touchpoint. Today, companies are facing increasingly sophisticated threat actors employing advanced AI capabilities, which require embracing new, AI-specific methods in addition to traditional security approaches.
In particular, leaders are highly concerned about the potential for threat actors to gain access to data and addressing other potential risks, such as hallucinations or data poisoning. That’s why, leaders are prioritizing the fundamentals even as they move forward with adopting AI. The primary consideration for executives when evaluating LLM providers is data privacy and security (37%), ahead of system integration and scalability and performance.
Manufacturers are no longer asking only what AI can do for them. The bigger question now is how to keep their data safe while using it. Every new AI initiative comes with the promise of efficiency gains, predictive insights, and smarter operations. But those benefits mean little if the systems you rely on expose your designs, formulas, or supply chain data to threat actors.
This shift is critical. AI adoption is no longer about speed or flashy features—it’s about trust. The companies that win are those that can prove their AI systems are secure, governed, and resilient. Security is not just a compliance checkbox; it’s becoming a competitive differentiator. When your customers, partners, and regulators know your AI systems are safe, they trust your brand more, and that trust translates directly into long-term business value.
Why Security Is the New AI Battleground
The rise of AI has created a new kind of arms race. Threat actors are no longer relying on outdated tactics; they’re using AI themselves to launch more sophisticated attacks. That means you’re not just defending against hackers—you’re defending against adversaries who have access to the same advanced tools you’re adopting.
Consider a manufacturer in the automotive sector. Imagine they’re using AI to optimize design simulations for new vehicle components. If attackers poison the model’s training data, the resulting designs could introduce flaws that compromise safety. The risk isn’t just financial—it’s reputational, regulatory, and potentially life-threatening.
Hallucinations and data poisoning are two terms you’ll hear often in this context. Hallucinations occur when AI generates false or misleading outputs, which can misguide decision-making. Data poisoning happens when malicious actors manipulate training data to corrupt AI models. Both risks are real, and both can directly impact your operations.
The insight here is simple but powerful: you’re not just protecting your systems from external threats, you’re protecting the integrity of your AI itself. That means your security strategy has to evolve. Traditional defenses like firewalls and encryption are still essential, but they’re no longer enough. You need AI-specific safeguards that anticipate how attackers might exploit the very models you’re deploying.
Why Privacy and Security Outrank Performance
When executives evaluate AI providers, the top priority is no longer speed or scalability—it’s privacy and security. In fact, 37% of leaders say data protection is their primary consideration, ahead of integration or performance. That statistic tells you everything about where the industry is heading.
Imagine a pharmaceutical manufacturer using AI to streamline quality checks. If proprietary formulas or clinical data were exposed, the financial loss could be immense, but the bigger issue would be trust. Regulators, partners, and patients would question whether the company could safeguard sensitive information. That’s why leaders are putting privacy and security ahead of performance metrics.
This shift represents maturity. Early adopters of AI often chased performance gains without fully considering the risks. Now, leaders understand that performance means nothing if intellectual property leaks or sensitive designs are compromised. The smartest manufacturers are treating AI providers less like vendors and more like custodians of their most valuable assets.
The conclusion is straightforward: choosing an AI provider is no longer about who has the fastest model. It’s about who can be trusted to protect your data. That’s the real ROI—because without trust, every other benefit of AI collapses.
Governance: The Invisible Backbone of AI Success
Governance is often misunderstood. Many see it as bureaucracy, but in reality, it’s the backbone of AI success. Governance is the set of rules, policies, and oversight mechanisms that prevent misuse and ensure reliability. Without it, even the best AI model becomes a liability.
Consider an electronics manufacturer using AI-driven predictive maintenance. If attackers gain access to sensor data, they could trigger false alarms or downtime. Strong governance ensures that every AI decision is traceable, auditable, and aligned with compliance standards. That way, even if something goes wrong, you can identify the issue quickly and prevent it from escalating.
Governance also builds trust internally. When your teams know that AI decisions are governed by transparent policies, they’re more likely to adopt and rely on those systems. That adoption is critical—AI only delivers value when people trust it enough to use it consistently.
The insight here is that governance isn’t a burden; it’s a framework for resilience. It ensures that AI systems are not only effective but also safe, ethical, and compliant. In a world where regulators are increasingly scrutinizing AI, governance is your shield against both external threats and internal missteps.
Comparing Priorities in AI Provider Selection
| Priority Factor | Why It Matters | Impact if Ignored |
|---|---|---|
| Data Privacy & Security | Protects IP, sensitive designs, and customer trust | Breaches, reputational damage, regulatory fines |
| Integration & Scalability | Ensures AI fits into existing systems | Operational inefficiencies, siloed data |
| Performance & Speed | Delivers faster insights and automation | Slower adoption, missed opportunities |
| Governance & Compliance | Keeps AI reliable, ethical, and auditable | Legal risks, loss of stakeholder trust |
Typical Risks in AI Adoption
| Risk Type | What It Looks Like | Business Impact |
|---|---|---|
| Hallucinations | AI generates false outputs | Misguided decisions, wasted resources |
| Data Poisoning | Malicious manipulation of training data | Compromised designs, safety risks |
| Unauthorized Access | Threat actors breach AI systems | IP theft, financial loss |
| Lack of Governance | No oversight or audit trails | Compliance failures, trust erosion |
What Leaders Are Doing Differently
Manufacturers are no longer satisfied with traditional security measures alone. They are layering AI-specific defenses on top of encryption, access controls, and audits. This shift reflects a deeper understanding: attackers are using AI to exploit vulnerabilities, so defenses must evolve to anticipate those tactics. You can’t rely on yesterday’s safeguards to protect tomorrow’s systems.
Consider a food manufacturer deploying AI to predict demand and optimize supply chains. If attackers manipulate the system to generate false forecasts, the company could face massive waste and financial loss. Leaders in this space are now requiring providers to demonstrate not only how their models perform but also how they resist manipulation. That’s a new kind of due diligence—one that prioritizes resilience over raw speed.
Another change is the way leaders are elevating AI security to board-level discussions. It’s no longer seen as an IT issue; it’s a business continuity issue. Imagine an electronics manufacturer whose predictive maintenance system is compromised. Downtime across multiple plants could cost millions per day. Leaders are treating these risks as central to their business models, not peripheral concerns.
The insight here is that the smartest manufacturers are building security into their AI strategies from the start. They’re not waiting for breaches to happen—they’re demanding transparency, audit trails, and governance frameworks upfront. That proactive stance is what separates those who thrive from those who stumble.
The Cost of Getting It Wrong
The risks of ignoring AI-specific security are not abstract—they’re tangible and immediate. A single breach can undo years of innovation, and the reputational damage often outweighs the financial loss. Customers, regulators, and partners all want assurance that your AI systems are safe.
Imagine a pharmaceutical manufacturer using AI to streamline quality checks. If attackers gain access to proprietary formulas, the fallout isn’t just financial—it’s regulatory investigations, lawsuits, and a loss of trust that could take years to rebuild. That’s why leaders are treating AI security as a business-critical priority.
Consider a textiles manufacturer using AI to forecast fashion trends. If competitors gain access to that data, they could launch similar products faster, eroding market share. The damage here isn’t about compliance—it’s about losing your edge in a highly competitive industry.
The lesson is simple: the cost of getting AI security wrong is far greater than the cost of investing in it upfront. Breaches don’t just hurt your bottom line; they undermine the trust that keeps your business viable.
Typical Consequences of Weak AI Security
| Risk Event | Immediate Impact | Long-Term Impact |
|---|---|---|
| Data Breach | Loss of IP, exposure of sensitive designs | Reputational damage, regulatory scrutiny |
| Model Manipulation | Faulty outputs, unsafe designs | Reduced trust in AI systems |
| Supply Chain Disruption | Delays, waste, financial loss | Loss of customer confidence |
| Compliance Failure | Fines, legal action | Ongoing restrictions, reduced market access |
Practical Steps You Can Take Tomorrow
Security isn’t abstract—it’s actionable. You can start with a checklist that prioritizes data protection when evaluating AI providers. Ask direct questions: How is your data stored? Who has access? What safeguards are in place against model manipulation?
Train your teams to recognize AI-specific risks. Hallucinations, data poisoning, and adversarial attacks aren’t theoretical—they’re real risks you need to prepare for. Imagine a food manufacturer whose AI mislabels allergens due to poisoned data. Training teams to spot anomalies could prevent a crisis before it escalates.
Build governance committees that include IT, operations, compliance, and legal. AI security is cross-functional, and you need diverse perspectives to anticipate risks. Consider an automotive manufacturer aligning AI governance with ISO and NIST standards. That alignment ensures compliance while building trust with regulators and partners.
The most important step is to act now. You don’t need to wait for a breach to start building safeguards. Proactive governance is always cheaper than reactive crisis management.
Security Actions You Can Implement Quickly
| Action | Why It Matters | How to Apply It |
|---|---|---|
| Demand Transparency | Ensures providers disclose how data is handled | Ask for audit reports and compliance certifications |
| Build Audit Trails | Tracks every AI decision | Require logs for all model outputs |
| Train Teams | Prepares staff for AI-specific risks | Run workshops on hallucinations and data poisoning |
| Align Governance | Keeps AI systems compliant | Map AI policies to existing standards |
3 Clear, Actionable Takeaways
- Make security your first filter when choosing AI providers. Don’t be swayed by performance claims until you’ve confirmed how your data will be protected.
- Build governance into your AI strategy from day one. Treat governance as a framework for trust—it’s what keeps your AI reliable and compliant.
- Train your teams to think in terms of AI-specific risks. Hallucinations, data poisoning, and adversarial attacks are real risks you need to prepare for.
Frequently Asked Questions
Why are manufacturers prioritizing security over performance in AI adoption? Because performance gains mean little if intellectual property or sensitive data is compromised. Security is now the deciding factor in provider selection.
What are hallucinations in AI, and why do they matter? Hallucinations occur when AI generates false or misleading outputs. In manufacturing, this can lead to flawed designs or misguided decisions.
How does governance improve AI adoption? Governance provides oversight, audit trails, and compliance frameworks that ensure AI systems are reliable, ethical, and safe.
What industries are most at risk from AI-specific threats? All manufacturing sectors face risks, but those handling sensitive formulas, designs, or supply chain data—such as pharmaceuticals, automotive, and electronics—are particularly exposed.
What practical steps can manufacturers take immediately? Start with a security-first checklist, demand transparency from providers, train teams on AI-specific risks, and align governance with existing compliance standards.
Summary
Manufacturers are embracing AI, but the real ROI now depends on how well they protect their data. Security and governance are no longer side issues—they’re the deciding factors in whether AI delivers lasting value. Leaders are prioritizing privacy and resilience over speed, recognizing that trust is the foundation of every successful AI initiative.
The risks are real: hallucinations, data poisoning, and breaches can directly impact safety, compliance, and competitiveness. But the solutions are within reach. By demanding transparency, building governance frameworks, and training teams to anticipate AI-specific threats, you can safeguard your future while unlocking the benefits of AI.
The message is simple: you don’t just need AI that works—you need AI you can trust. Manufacturers who act now, embedding security and governance into every decision, will be the ones who thrive in the next wave of AI adoption.