Manufacturing has always operated on tight margins and tighter timelines. A disrupted production line doesn’t just cost money; it breaks contracts, stalls supply chains, and damages reputations built over decades. So when a technology arrives that promises to optimise operations, reduce downtime, and improve quality control, manufacturers adopt it fast.
That technology is AI. And the speed of that adoption is creating a serious problem.
Today’s factory floor sits at the centre of a genuine paradox: AI is simultaneously the most powerful tool available to defend manufacturing operations against cyberattacks and one of the fastest-growing reasons those operations are being targeted. Understanding both sides of this tension is the defining cybersecurity challenge for manufacturers in 2026.
Why AI Has Become Central to Manufacturing Operations?
The rise of AI in manufacturing has been steep and, in many cases, outpaced the infrastructure needed to support it securely. The numbers reflect how fast this has moved:
- Over 40% of manufacturers with production scheduling systems have already upgraded to AI-driven platforms
- By 2029, IDC projects 30% of factories will run on centralised, software-defined automation platforms
- 44% of manufacturing CEOs discussed active AI initiatives in their Q4 2025 earnings calls, a 35% year-on-year increase
The business case is clear. AI improves throughput, enables predictive maintenance, reduces waste, and tightens supply chain coordination. The top three use cases driving adoption across industrial operations right now are process automation, quality inspection, and logistics optimisation.
But as AI embeds itself deeper into operational technology (OT) environments, the systems controlling machines, assembly lines, and industrial processes, it is expanding the attack surface in ways many manufacturers have not fully accounted for.
The Threat Side of the Paradox
Attackers are not passive observers of this shift. They are exploiting it actively.
Modern production environments tightly link OT systems with IT networks, design software, and IoT-enabled equipment. This IT/OT convergence, while essential for efficiency, has dissolved the air gaps that once kept factory floor systems isolated from external threats. Legacy industrial control systems, never designed with network connectivity in mind, are now exposed to the same threat landscape as any enterprise IT environment.
The consequences are already being felt at scale:
- Manufacturing has held its position as the world’s most ransomware-targeted industry for five consecutive years
- Attacks increased 61% year over year in 2025
- Over 90% of total financial losses in the sector are attributable to ransomware, despite it representing only 12% of claims by volume.
- The average cost of production downtime during a manufacturing cyberattack runs into millions per day.
AI has made the attacker’s job measurably easier. AI-powered tools now allow threat actors to execute attacks with greater speed and precision than ever before. Autonomous attack chains can target endpoints continuously, adapting tactics in real time without human intervention. The skill barrier for launching sophisticated attacks has dropped sharply, and the volume of cyber activity across industrial sectors has risen in direct proportion.
In practical terms, this means a manufacturer deploying AI across its production systems without equivalent security investment is not just accepting more risk — it is becoming a more attractive, more accessible target.
The Defence Side: Why AI Is Also the Answer
Here is where the paradox sharpens into something actionable.
The same capabilities that make AI dangerous in the hands of attackers make it indispensable in the hands of defenders. AI-powered detection and response tools can analyse network behaviour, flag anomalies, and identify threats at a speed and scale no human security team can match. By 2029, IDC predicts that 75% of large manufacturers will use AI-powered cyber defence as their primary method for detecting threats faster and with less manual effort.
For OT environments specifically, AI delivers capabilities that traditional security tools cannot:
- Passive asset discovery maps every connected device across a production environment, including unmanaged IIoT devices that have historically created invisible security blind spots, without disrupting operations
- Predictive vulnerability management uses global threat intelligence and exploit trend analysis to identify which security flaws are likely to be weaponised, hence teams can prioritise patching before an incident rather than after
- Behavioural anomaly detection establishes baseline patterns for industrial systems and flags deviations that signature-based tools would miss entirely.
The critical point is this: AI is not just a useful addition to manufacturing cybersecurity. For the scale and complexity of modern industrial environments, it is becoming a foundational requirement.
The Organisational Gap That Puts Everything at Risk
Understanding the technology on both sides of the paradox is only part of the challenge. The deeper problem is structural.
Only 20% of organisations report fully collaborative IT/OT interworking on cybersecurity. This is a critical gap, because meaningful AI-powered defence requires both teams working from a unified security posture. When IT and OT operate in silos with separate ownership, separate tooling, and separate risk frameworks, visibility breaks down, and threats that cross the boundary go undetected until the damage is done.
The most common and costly attack sequence looks like this:
- Attackers compromise an IT entry point, typically through phishing or a third-party vendor
- They move laterally across the network, exploiting the absence of segmentation between IT and OT
- They reach production control systems with no monitoring in place to detect or block them
- The production line is halted, often for days, before the full scope of the breach is understood
Boards and executive teams are increasingly aware of this dynamic. Risk quantification is shifting from abstract technical metrics toward operational consequences: uptime loss, safety exposure, production quality degradation, and regulatory liability. These are terms that resonate at the board level, and they are creating pressure for integrated security investment that spans both IT and OT functions.
What Manufacturers Must Do in 2026?
The organisations reducing their exposure right now share a common approach. They are not choosing between deploying AI and securing their environments. They are treating security as the prerequisite for AI adoption, not something to address afterwards. In practice, this means five things:
1. Establish unified IT/OT visibility first
You cannot defend what you cannot see. Comprehensive asset discovery across both environments, including legacy OT devices, unmanaged IIoT endpoints, and third-party integrations, must precede scaling AI workloads. Organisations evaluating IT solutions for manufacturing should prioritise platforms that provide cross-environment visibility as a baseline capability, not an add-on.
2. Segment OT networks using a recognised framework
Implementing an Industrial DMZ between enterprise IT and production OT, aligned to the ISA/IEC 62443 zones-and-conduits model, is the single highest-impact architectural investment most manufacturers can make. It limits the blast radius of any incident, preventing lateral movement from a compromised email account from reaching a production control system.
3. Keep humans in the loop on AI-driven decisions
AI is not a set-and-forget control. Security teams must verify and validate AI-driven alerts and responses using formal quality assurance processes. In OT environments, a false positive that triggers an automated shutdown can be as damaging as the attack itself.
4. Enforce foundational controls without exceptionÂ
MFA misconfiguration is a leading driver of costly breaches, and unpatched software is directly linked to the most expensive outcomes. AI-powered monitoring does not compensate for gaps in basic hygiene. Multi-factor authentication, regular patching, and tight access controls must be enforced across both IT and OT environments, including vendor access.
Build for resilience, not just prevention. Preventing every attack is not a realistic objective. The focus must shift toward rapid detection, containment, and recovery. OT-specific incident response playbooks, ones that prioritise production safety and continuity, not just data recovery, are non-negotiable in 2026.
The Competitive Dimension
There is one more dimension to this paradox that manufacturers cannot afford to ignore.
Cyber resilience is becoming a commercial differentiator. Customers, insurers, and supply chain partners are increasingly scrutinising the security posture of the manufacturers they work with:
- Cyber insurers are tightening coverage requirements and raising premiums for organisations that cannot demonstrate strong controls
- Enterprise customers are beginning to impose contractual security requirements on their manufacturing partners
- Regulators are introducing mandatory vulnerability reporting and compliance deadlines that make security posture a legal exposure, not just an operational one
The manufacturers who get this right, who build AI-powered defence into their operations rather than treating it as an afterthought, will be better positioned not just to survive attacks, but to win business from competitors who have not.
The AI paradox on the factory floor is real. But it is resolvable. The answer is not to slow down AI adoption. It is to make security the foundation on which that adoption stands.
