Connected devices are now the fabric of modern operations, from smart buildings and retail endpoints to hospital equipment and factory sensors. As organizations wire up fleets of cameras, thermostats, robots, meters, and gateways, AI is increasingly the brain that tries to keep all of that safe.
Done well, AI is the only scalable way to continuously inventory assets, read weak signals in messy telemetry, and trigger the right response in seconds. Done poorly or attacked, it can become the shortest path from "lights on" to "lock the doors."
Below is a comprehensive look at where AI meaningfully raises the bar for IoT security, where it can cut the other way, and what CISOs should do about it.
AI as the great enabler
IoT environments are noisy, heterogenous, and often opaque. Traditional signature-first defenses struggle with weird protocols, legacy stacks, and custom firmware. AI changes that equation in three practical ways.
First, it learns "normal" in places where baselines are elusive. Unsupervised and self-learning models can profile device behavior without needing pristine training data. This helps pinpoint anomalies such as a badge reader that suddenly talks SMB protocol that it's normally not supposed to talk, or a camera beaconing on an unusual port. Furthermore, machine learning can classify botnet traffic and spot deviations in real time, even on constrained networks.
Second, AI scales threat hunting. Models that cluster flows and compress high-volume telemetry help white hats pivot from millions of logs to a handful of interesting outliers. Platforms like Microsoft Defender for IoT can already pipe detections into Security Operations Center (SOC) workflows so anomalies become incidents with playbooks attached.
Third, AI makes sense of messy signals. In healthcare's Internet of Medical Things (IoMT), for example, research shows AI-enhanced monitoring can improve early detection while respecting clinical constraints and device limitations—exactly where agents and signatures fall short.
The net result is that with the right data and guardrails, AI provides continuous, context-aware detection that keeps pace with device sprawl instead of falling behind it.
The double-edged sword
Ironically, the same techniques that help defenders also help attackers. Criminals are automating reconnaissance, targeting exposed protocols common in IoT, and accelerating exploitation cycles. Fortinet recently highlighted a surge in AI-driven automated scanning (tens of thousands of scans per second), where IoT and Session Initiation Protocol (SIP) endpoints are probed earlier in the kill chain. That scale turns "long-tail" misconfigurations into early footholds.
Worse, AI itself is susceptible to attack. Adversarial ML (machine learning) can blind or mislead detection models, while prompt injection and data poisoning can repurpose AI assistants connected to physical systems. A 2024 survey of adversarial attacks against ML-based network intrusion detection shows practical paths to evade models that many teams now rely on for IoT visibility. If your SOC treats the model as an infallible entity, you're potentially one bad input away from missing a breach.
Strategic implications for CISOs
CISOs can't simply "add AI" and declare victory. IoT security with AI demands systems thinking across data, models, and response.
Treat telemetry as a product. Curate data pipelines so models see the right things: device identity, protocol metadata, passive DNS, DHCP, EDR/XDR context, and asset-owner tags. Good data is the first control plane for AI; poor data is a vulnerability, not a feature. Therefore, it's important to prioritize data quality when working with IoT software development services, from the prototyping and design stage all the way to deployment.
Engineer model trust like you engineer zero trust. Assume models can be fooled. Apply model-ops guardrails such as drift detection, confidence scoring, adversarial testing, and "kill switches" that degrade to deterministic rules when inputs look poisoned. Pair unsupervised anomaly detection with explainability so analysts aren't forced to escalate every black-box alert.
Move response left. Anomaly detection without orchestration just creates work. It's important to pre-stage responses such as quarantine VLANs, Access Control List (ACL) updates, Network Access Control (NAC) policies, and maintenance window tickets. This way, high-confidence detections contain first and ask questions second.
Finally, run purple-team exercises that assume AI is the target and the tool. This includes simulating prompt injection against your assistants and dashboards; simulating adversarial noise against your IoT Intrusion Detection System (IDS); and testing whether analysts can distinguish "model weirdness" from real incidents under time pressure.
Case studies in success and failure
The impact of AI in IoT security isn't limited to theory. A few events that hit the headlines over the past years illustrate how the technology can both strengthen defenses and give attackers the upper hand. Let's look at real-world examples of these two extremes.
Darktrace reported an incident in which self-learning AI spotted and autonomously contained an attack against a national sporting body ahead of the 2021 Tokyo Olympics. The environment included IoT/OT devices; anomaly-driven detections curtailed lateral movement and command-and-control before attackers could weaponize connected infrastructure. The lesson: in sprawling, heterogenous estates, AI that understands "normal" can buy precious minutes and suppress blast radius without brittle signatures.
On the other hand, we've seen ways threat actors could weaponize AI. In August 2025, researchers showed how a poisoned Google Calendar entry could hijack a Gemini-powered smart home. When the user asked the AI to summarize their schedule, hidden instructions triggered real-world actions such as opening shutters, toggling heat, and more. No malware required, just poisoned data in a trusted workflow. Google added mitigations, but the takeaway for enterprise IoT is stark: once AI brokers commands between users and devices, indirect inputs (docs, invites, tickets) become an attack surface, and criminals will copy working techniques.
The path forward
If AI can make or break IoT security, your job is to make the pendulum predictably swing towards the positive outcome. Here's what that means in practical terms.
-
Keep track of your IoT ecosystem. Build and maintain a living inventory with owner, function, network zone, firmware lineage, and support window. AI amplifies whatever you feed it, and unknown devices along with stale metadata cripple even the best models. Tie inventories to risk scoring so model outputs drive action rather than curiosity.
-
Prioritize explainability. Prefer models that provide feature importances or natural-language rationales for anomalies. Analysts need to see why a pump controller is suspicious (new DNS pattern, time-of-day deviation) to diagnose confidently and tune false positives without turning everything back into signatures.
-
Secure the AI control plane. Treat prompts, training data, and model connectors as sensitive assets. Sanitize external inputs (tickets, documents, calendar items), enforce explicit user confirmation for high-risk actions, and keep model permissions least-privileged. Consider red-teaming prompt injection against any AI that can touch environmental controls.
-
Automate containment, not judgment. Use AI to trigger deterministic controls such as NAC moves, micro-segmentation, and rate limits, while humans should validate root cause. This balance keeps dwell time low without outsourcing decisions that could trip safety systems.
Keep in mind that AI is not a magic shield for insecure devices; it's a force multiplier for disciplined programs. In IoT, that discipline starts with inventory, segmentation, and lifecycle hygiene, and then uses AI to watch the seams no human team can. Treat models as fallible teammates that need guardrails, and they will keep your lights on rather than switch them off.
