Data security is critical for IoT to flourish as lack of consumer confidence can impede adoption. Machine Learning enables expedited threat detection, investigation, and remediation for safer and more robust IoT deployments.

The cybersecurity risk posed by IoT will increase dramatically as the number of IoT-connected devices proliferate worldwide, driving an ever-expanding attack surface that malicious actors will seek to exploit.

Today’s IoT security risks are enormous, with new vulnerabilities always surfacing. Existing solutions tackle parts of the problem. Startups are using Machine Learning to cover the entire IoT security waterfront.

The speed of adoption and devices will continue, and the awareness of the aspects of IoT to be wary of will drive the push for increased security built in to the devices.

IoT devices are currently being developed inherently insecurely. With the number of devices exploding, IoT will morph towards self detection and remediation underpinned by machine learning capability.

8 out of 10 IoT devices are insecure – we’ll start seeing anomaly based detections start to pick up abnormal behaviour in networks and systems.

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