AI Threat Detection for Manufacturers Under Attack

Manufacturing has become the ransomware economy’s favorite pressure point. In 2025, 51% of manufacturers hit by ransomware paid, with an average $1 million ransom and $1.3 million in recovery costs on top of it. That’s before you count lost output, missed ship dates, expedited logistics, contractual penalties, and the long tail of “we’re still rebuilding” downtime.

What’s changed isn’t just the volume of attacks. It’s the way compromises happen. Exploited vulnerabilities became the most common root cause of manufacturing compromises again in 2025, after malicious email and credential theft led in prior years. Translation: attackers aren’t only tricking users. They’re walking through doors that should’ve been locked.

This post is part of our AI in Cybersecurity series, and I’ll take a clear stance: manufacturers can’t patch-and-pray their way out of this. The only workable path is to combine OT-safe fundamentals (segmentation, backups, access control) with AI-powered threat detection and response that can keep pace with modern attackers—especially in hybrid IT/OT environments where visibility is messy and time-to-impact is short.

Why manufacturers are the top target (and why it keeps working)

Manufacturers get targeted because attackers can reliably force urgency. When ransomware hits a plant, it doesn’t just slow email—it halts production lines, idles labor, and triggers cascading supply chain issues. That’s why threat actors expect payment pressure to be higher in industrial environments than in many office-first sectors.

Three conditions make this worse in manufacturing:

• Operational disruption is immediately monetizable. A stopped line is a measurable dollar loss per hour.
• IT/OT boundaries have eroded. Remote access, data lakes, and analytics initiatives create pathways between enterprise systems and plant systems.
• Security expertise is thin. Many manufacturers cite lack of security expertise, unknown gaps, and missing protections as core breach drivers.

This matters because most “reasonable” security programs were designed for office networks. Manufacturing needs security that respects real constraints: uptime requirements, legacy devices, vendor-managed systems, and safety implications.

The cost story isn’t just ransom

Ransom is the headline, but it’s rarely the biggest line item.

A major attack can trigger:

• Extended outage and scrap (partially completed runs, spoiled materials, failed QA)
• Manual workarounds (paper processes, overtime, rework)
• Expediting costs (premium freight, alternative suppliers)
• Regulatory and contractual exposure (especially in automotive, aerospace, pharma)
• Rebuild costs (re-imaging, restore validation, OT re-certification)

One widely reported case in 2025 shut down production for weeks and carried estimated losses in the billions. Even if your business is smaller, the pattern holds: downtime compounds.

The attack shift: exploited vulnerabilities are back on top

When exploited vulnerabilities rise to the top root cause, it usually means two things are true:

1. Attackers have improved reconnaissance and automation. They’re faster at finding internet-exposed services, unpatched edge devices, and weak vendor access paths.
2. Defenders can’t keep up with remediation. Plants run 24/7, patch windows are limited, and ownership is split across IT, OT, engineering, and third parties.

In manufacturing, vulnerability exploitation is especially dangerous because an attacker doesn’t need to “live off the land” for weeks. Once they have an initial foothold, they can move quickly toward:

• Active Directory and identity systems
• Backup infrastructure
• Remote management tools
• File shares tied to production planning and quality
• OT jump hosts, historians, and engineering workstations

The uncomfortable truth about “critical vulnerability” metrics

Many industrial orgs have a long tail of high-severity findings. But severity scores don’t always match operational risk.

A better way to prioritize is:

• Exploitability in the wild (are campaigns already using it?)
• Exposure (internet-facing, vendor VPN/RDP, flat network reachability)
• Impact path (can it reach identity, backups, or OT control zones?)

This is where AI can help in a practical, non-hype way: triage and correlation. Not “AI patches your PLC,” but AI that tells you which 20 of your 2,000 findings are most likely to become a plant outage.

AI in cybersecurity for OT: where it actually earns its keep

AI is valuable in manufacturing security when it reduces time-to-detect and time-to-contain without adding fragile complexity.

A simple, defensible statement is:

AI is best at finding patterns humans miss and prioritizing actions humans can’t scale.

That applies strongly to industrial environments, where signals are noisy and staffing is limited.

Use case 1: anomaly detection that understands “normal” in a plant

Traditional detection rules often fail in OT because “normal” is weird:

• Shift changes create traffic bursts
• Maintenance windows introduce unusual tools
• Vendors remote in intermittently
• Legacy protocols look suspicious to IT-centric tools

AI-based anomaly detection can model baselines per site, per cell, and per asset group, then flag deviations such as:

• A PLC engineering workstation authenticating to new systems
• Lateral movement from IT subnets into OT segments
• Sudden authentication storms (password spraying) against OT-adjacent services
• Unexpected data exfiltration from historians or quality systems

The goal isn’t to alert on everything. It’s to catch the handful of behaviors that correlate to ransomware staging: credential access, privilege escalation, backup targeting, and mass encryption prep.

Use case 2: faster incident response when minutes matter

Manufacturing response often stalls on one question: “Can we isolate this without stopping production?”

AI-assisted response can:

• Summarize the incident timeline (what happened first, what spread, what’s next)
• Recommend containment options tied to known dependencies (block a host, disable an account, isolate a VLAN)
• Reduce analyst fatigue by clustering duplicate alerts and highlighting the true pivot points

In practice, the biggest value is decision support. I’ve seen teams lose half a day debating whether an alert is “real.” In manufacturing, half a day can be the difference between a local containment event and a full-site shutdown.

Use case 3: risk-based vulnerability prioritization (not just scanning)

Vulnerability management in manufacturing breaks when it becomes a monthly PDF.

AI-driven prioritization works when it merges:

• Known exploited vulnerabilities
• Asset criticality (line impact, safety relevance)
• Exposure paths (reachable from business networks, vendor access, remote services)
• Compensating controls (segmentation, allowlists, MFA)

The output should be operational:

• “Patch these 5 systems this week.”
• “These 8 can’t be patched—add segmentation and monitoring.”
• “These 3 vendor paths need immediate tightening.”

That’s how you reduce risk without demanding impossible patch cycles.

AI adds attack surface too—so build guardrails on purpose

Manufacturers are adopting AI for robotics, predictive maintenance, and optimization. More data. More integrations. More endpoints. More identity sprawl.

If you’re adding AI to OT, assume attackers will target the new seams:

• Data pipelines from plant floor to analytics platforms
• Model training data and labeling systems
• Agentic workflows that trigger actions automatically
• Shared service accounts and API keys

A practical stance: treat AI components like critical infrastructure. They need the same discipline as MES, historians, and remote access.

OT-ready guardrails that reduce AI risk

If you’re integrating AI into industrial environments, the security checklist that actually helps looks like this:

1. Segment AI data flows (plant → DMZ → enterprise/cloud). No direct “flat” routes.
2. Strong identity controls for machines and services: MFA for admins, least privilege for service accounts, short-lived tokens where possible.
3. Monitor for data access anomalies: bulk reads, unusual query patterns, access from new geographies or hosts.
4. Change control for models and automations: approvals, versioning, rollback plans.
5. Test incident response on OT scenarios: isolation procedures, manual operation fallback, restore validation drills.

Those guardrails align with emerging government and industry guidance around AI in OT: the theme is consistent—integrate AI, but don’t bypass basic controls to do it.

A practical 90-day plan: AI-powered defense without breaking production

Manufacturers don’t need a massive multi-year transformation to reduce ransomware risk. You need a focused plan that combines fundamentals with AI-assisted detection and triage.

Days 0–30: get visibility and stop the obvious bleeding

• Inventory critical OT-adjacent assets: domain controllers, backup servers, jump hosts, engineering workstations
• Review remote access paths: vendor VPNs, RDP, remote management tools
• Enforce MFA for all privileged access and remote access (including vendors)
• Turn on centralized logging for identity and endpoint events where feasible
• Validate backups by restoring into a test environment (not just “backup succeeded”)

Days 31–60: deploy AI where it reduces workload immediately

• Enable AI-assisted alert triage in your SOC workflows (case summarization, alert clustering)
• Stand up anomaly detection for east-west movement and identity behavior
• Implement risk-based vulnerability prioritization tied to exploitability and exposure
• Build a “stop-the-line” playbook: what gets isolated first, who approves, what’s the fallback

Days 61–90: harden the IT/OT boundary and automate safe responses

• Strengthen segmentation between IT and OT with monitored choke points
• Introduce allowlists for critical OT zones and limit unnecessary protocols
• Automate low-risk actions: disable clearly compromised accounts, quarantine infected endpoints in IT zones, block known malicious domains
• Run a ransomware tabletop that includes engineering, plant leadership, IT, OT, and legal

If you do only one thing: make ransomware containment a rehearsed process, not an improvised debate. AI helps most when it’s plugged into a plan.

What to ask when you’re evaluating AI threat detection for manufacturing

If you’re shopping for AI-powered threat detection, the questions that separate real capability from marketing are straightforward:

• Can it distinguish IT vs OT context? (or will it flood you with false positives)
• Does it support passive monitoring for OT? (active scanning can be risky)
• How does it handle identity-based attacks? (ransomware often pivots through credentials)
• Can it show an incident story quickly? (timeline, affected assets, likely next steps)
• What response actions are safe to automate in a plant environment?

A good tool doesn’t just “detect threats.” It helps you decide what to do next, fast.

The manufacturing reality: AI is becoming mandatory for defense

Manufacturing is adopting AI for productivity, so attackers are adapting too. More connectivity and automation increases the number of ways a compromise can cascade from a single vulnerable edge device into a plant-wide outage.

AI won’t replace segmentation, backups, or disciplined access control. But it does something manufacturers badly need: it scales expertise. When staffing is thin and environments are complex, AI-powered threat detection and response can help you spot the early signals of ransomware staging and contain it before production is on the floor.

If your 2026 plan is “we’ll patch faster,” you’re already behind. Build a security program that assumes exploitation will happen—and invest in AI threat detection for manufacturing that shortens the time between first signal and containment.

Where would a ransomware team hit you first: remote access, identity, or backups? If you can’t answer that confidently, that’s the first gap to close.