Manufacturers’ AI Adoption is Outpacing Cybersecurity, Compliance, and Risk Governance
A new report from Kiteworks finds that manufacturers lead in operational AI controls but remain underprepared for adversarial AI attacks, regulatory audits, and third-party AI failures across global supply chains.
Key Takeaways
- Operational AI controls are strong, but narrowly focused. Most manufacturers maintain human oversight and real-time monitoring of AI systems, reflecting a deep commitment to uptime, safety, and production reliability.
- Adversarial AI testing is a major blind spot. Only 7% of manufacturers conduct AI red teaming or adversarial testing, leaving production, quality, and supplier-driven AI systems vulnerable to intentional cyberattacks.
- Compliance readiness lags AI deployment. Limited use of privacy impact assessments and audit-quality evidence could expose manufacturers to regulatory action as AI governance requirements expand globally.
- Third-party AI risk is emerging as a systemic supply chain threat. AI failures at suppliers, logistics partners, or technology vendors are increasingly likely to disrupt manufacturing operations without clear accountability or governance.
Current State of AI Governance in Manufacturing
According to Kiteworks, manufacturers’ rapid adoption of artificial intelligence is outpacing their ability to govern AI-driven cyber and supply chain risk. The report, Data Security and Compliance Risk: 2026 Forecast Report, notes that while manufacturers lead in operational AI controls, they are underprepared for adversarial AI attacks and regulatory scrutiny.
The findings are based on a global survey of 225 security, IT, compliance, and risk leaders, including 27 from manufacturing organizations. Manufacturers outperform global peers in production-critical AI controls, with 63% maintaining human oversight and 56% monitoring AI data flows.
Emerging Cyber Blind Spots
Despite these strengths, manufacturers are not resilient against intentional cyber threats. Only 7% conduct AI red teaming or adversarial testing, significantly expanding the manufacturing attack surface. As Tim Freestone, chief strategy officer at Kiteworks, notes, “Manufacturing has built AI governance for reliability, not hostility.”
Compliance and Audit Readiness Gaps
The report highlights significant compliance gaps, with only 15% of manufacturing organizations conducting privacy impact assessments. Without strong documentation and audit trails, manufacturers may struggle to demonstrate compliance with emerging AI regulations.
Kiteworks warns that while manufacturers may detect AI-related anomalies through monitoring, weak audit trails will limit their ability to investigate root causes and explain outcomes to regulators.
Third-Party AI Risk as a Systemic Threat
A central concern is the growing gap between internal AI governance and third-party AI risk. AI systems used by suppliers and logistics partners often lack equivalent governance, increasing the likelihood of production disruptions.
Patrick Spencer, SVP of Americas marketing at Kiteworks, states, “When supplier AI systems fail, the impact shows up on the production line, not in a policy document.”
Five AI Risk Predictions for Manufacturers in 2026
Kiteworks outlines five predictions manufacturers should act on immediately:
- Adversarial AI attacks will exploit testing gaps. With 93% lacking adversarial testing, AI systems will be targeted through model poisoning and data manipulation.
- Compliance documentation gaps will drive regulatory exposure. Limited privacy impact assessments will increase enforcement and reputational risk.
- Monitoring will outpace forensic readiness. Manufacturers will detect incidents but lack the data needed to investigate.
- OT-AI convergence will outgrow IT-centric governance. Traditional IT governance frameworks will fall short as AI integrates deeper into operational technology.
- Third-party AI failures will disrupt production. Supplier and partner AI risks will remain under-governed, with minimal oversight.
Closing the Gap Between Operational Excellence and AI Resilience
Kiteworks recommends manufacturers extend existing safety and quality disciplines to AI governance by:
- Implementing adversarial AI testing programs
- Strengthening compliance documentation and audit trails
- Building forensic-ready incident response capabilities
- Developing AI-specific OT governance models
- Elevating supply chain AI risk to board-level oversight
In conclusion, manufacturers need to adapt their operational DNA to include adversarial AI risk, regulatory proof, and supply chain accountability. Those who adapt will lead, while those who do not may face significant disruptions.
