Without strict security governance, AI could become a liability

What you’ll learn:

With the swell of data generation, AI plays an increasing role in ingesting and decoding information.
Manufacturers integrate and orchestrate AI without comprehensive oversight of all the potential vulnerabilities.
To effectively mitigate regulatory, security and accuracy risks in AI-powered tools, organizations need to consider how to employ a structured, comprehensive governance approach.

Over the last 20 years, manufacturing has become an increasingly data-intensive industry, generating 1,812 petabytes (PB) of data annually and surpassing several other “big data” sectors like finance, retail and communications.

The acceleration of digital systems and IoT-connected equipment has given manufacturers greater visibility into operations, supply chains and production cycles.

With the swell of data generation, AI plays an increasing role in ingesting and decoding information, enabling companies to optimize processes and tackle challenges that were once beyond reach. It’s no surprise then that 93% of manufacturers view AI as essential to progress, according to Deloitte.

Manufacturers have long used machine learning for factory automation, order management and production scheduling. More advanced predictive and generative applications have expanded into supply chain logistics, quality control and proactive maintenance.

AI-powered tools help reduce downtime, detect defects and improve demand forecasting, allowing for more agile and efficient operations. With the high volume of data flowing through manufacturing ecosystems, AI is a powerful addition to manufacturers’ toolsets for making sense of patterns and responding to real-time challenges.

While AI has proven itself capable of delivering significant advantages, fast-paced adoption has introduced an array of new security and compliance risks.

In some cases, manufacturers integrate and orchestrate AI without comprehensive oversight of all potential vulnerabilities, leaving them exposed to regulatory penalties, cyber threats and costly operational disruptions.

Without structured governance, AI can easily become more of a liability than an asset. As AI continues to shape the industry, manufacturers must balance innovation with risk management to lay the foundation for long-term success.

Four tactics for a proactive approach

To effectively mitigate regulatory, security and accuracy risks in AI-powered tools, organizations need to consider how to employ a structured, comprehensive governance approach. These strategies position manufacturers to protect their AI investments and set them up for success.

Integrated risk management

Manufacturers with AI-powered tools that cover multiple departments need a full view of potential risks. A holistic governance, risk and compliance (GRC) system provides complete oversight of the entire operation. A centralized source of truth for AI-related risk information across all use cases allows for consistent risk tracking, policy enforcement, and standardized controls.

Diligent and careful documentation, including reporting data sources, model training and improvement processes, evaluation results and any changes made to the AI system over time, is a vital step in demonstrating regulatory compliance (e.g., GDPR, CCPA) and internal accountability.

Incident response plans should determine the plan of action for identification, extermination, recuperation and analysis. They need to also address policies and guidelines for AI-driven cyberattacks, which differ from traditional security breaches.

Risk assessment frameworks locate possible vulnerabilities pre-deployment, examining the quality of data, hostile breaches, unexpected consequences and model bias.

Real-time compliance tracking

As regulations continue to shift, automated compliance tracking and reporting is crucial in protecting businesses from legal and financial consequences.

Automated compliance tools can generate comprehensive regulatory adherence reports with complete visibility over compliance status and search for potential oversight.

Before risks turn into violations, automated compliance reporting immediately notifies all stakeholders to enable better decision-making.

Integrating this proactive approach is essential for AI-powered tools to perform as they are designed to function. By establishing proper boundaries, manufacturers can prevent disruptions and build trust.

Validating data

Careful examination is crucial to establishing standards for data integrity and maintaining fairness, bias and regulation compliance. AI’s “black boxes” can be navigated more transparently by adhering to best practices for conducting audits on AI models. Constant reviewing and confirming procedures certify AI’s reliability and prevent the tool from becoming a source of misinformed decision-making.

Real-world trials can be used to evaluate AI systems and detect errors and biases.

To reflect the present state of the industry, training datasets must be continuously updated.

Feedback loops can be deployed to check the accuracy of AI decisions with human experts.

Prioritizing security

With manufacturers relying more on AI-powered tools, security must be established from the very beginning. Processing a massive amount of sensitive data, AI systems become prime targets for cyberattacks that manipulate algorithms or extract valuable information.

This means manufacturers are best served by establishing a cybersecurity-first culture when deploying AI tools—the integrity of their data relies on it.

This proactive mindset stops security attacks at the door through embedded protection processes, established directly into the AI development and launch, rather than prioritizing functionality and putting security on the back burner.

To stop unauthorized access, manufacturers need to encrypt data produced by AI tools, implement multi-factor authentication and create custom guardrails to ensure security and regulatory compliance.

The competitive advantage of AI risk management

As AI's role in manufacturing continues to grow and evolve, so do the risks to data privacy and regulatory compliance. To effectively manage and mitigate these risks and take advantage of AI’s full potential, manufacturers should strongly consider proactively implementing AI governance within a centralized GRC system.

This allows them to gain a competitive edge by establishing reliability, compliance and security across all branches of tech-enabled manufacturing operations.

By not taking a proactive approach to business risk, AI tools can undermine an organization’s security posture, open the door to costly compliance consequences and become targets for cyberattacks. Manufacturers that embed proper risk management protocols and procedures into their AI strategies will be best positioned for long-term success.