AI is redefining the foundations of cybersecurity — not in abstract ways, but in the day-to-day reality of how organisations identify, authenticate and protect connected devices (commonly referred to as the Internet of Things (IoT)). As artificial intelligence becomes a primary driver of change, security teams must adapt to new realities. As connected systems expand across hospitals, manufacturing plants, vehicles, energy grids and enterprise networks, many IoT devices are deployed at scale, creating an impossible challenge: millions of devices, fragmented ecosystems, and a growing landscape of cybersecurity threats, with threat actors who now use AI themselves to accelerate attacks.

Device Authority’s July newsletter revealed that KeyScaler 2025 is now underpinned by AI-supported automation, enabling organisations to simplify lifecycle management for unmanaged devices and strengthen trust decisions across complex environments. This reflects a deeper industry shift: AI has become essential to securing IoT and OT, not a futuristic add-on.

This article explains how AI is transforming machine identity, Zero Trust, anomaly detection and IoT lifecycle security — and why automation is now the only realistic approach for securing connected ecosystems at scale.

The New Reality: IoT and OT Have Become Too Big to Secure Manually

IoT and OT environments look nothing like traditional IT networks. They contain:

• Device types with wildly inconsistent security models
• Legacy equipment that cannot be patched
• Consumer-grade IoT brought onto corporate networks
• Critical OT systems that cannot tolerate downtime
• Devices that cannot run agents or support traditional monitoring
• Thousands to millions of identities that need continual validation
• Interconnected IoT systems that require comprehensive security across devices and infrastructure

Security teams cannot manually:

• Track firmware versions
• Rotate credentials
• Analyse telemetry
• Check for behavioural anomalies
• Assess compliance
• Discover shadow devices
• Respond to threats in real-time
• Identify and mitigate potential security threats from unmanaged or diverse devices

The sheer scale, diversity and velocity of these environments have made manual security impossible. AI fills this gap by providing speed, precision and automation that humans cannot match. Automation ensures that machine identities are properly managed throughout their lifecycle, preventing vulnerabilities and maintaining robust security.

AI’s Most Transformative Role: Intelligent Machine Identity Management

Traditionally, machine identity was treated as a static object — a certificate or key issued once and rarely revisited. But in IoT/OT environments, identity is more dynamic, and digital credentials form the foundation of machine identity. Devices change firmware, join new networks, update capabilities, or shift risk profiles over time.

AI allows machine identity to become contextual, continuous, and adaptive. It can monitor and respond to changes in device identity and the lifecycle of machine credentials, including their issuance, renewal, and revocation.

This approach covers not only human users but also non human identities, such as service accounts and machine identities, which are critical in IoT/OT ecosystems.

By leveraging AI, organizations can automate and scale identity management, reduce manual errors, and improve security posture. Managing machine identities is essential for enhancing security, operational efficiency, and automation across complex environments.

1. AI-generated trust scores

AI evaluates multiple factors — certificate validity, firmware integrity, device behavior, vulnerability exposure and historical patterns — to produce a real-time trust score for each device using advanced AI models.

Instead of simply asking “Does this certificate exist?”, AI answers a deeper question: “Is this device behaving in a way that can be trusted right now?” AI models analyze data from devices, including real-time activity and historical information, to assess trustworthiness and detect anomalies.

2. Continuous assessment instead of static onboarding

Certificates are no longer granted indefinitely. AI continually reassesses whether a device deserves access, incorporating secure authentication as part of the process.

Access is granted or revoked dynamically, with access management enforced by AI to ensure only authorized devices maintain connectivity.

3. Automatic enforcement

If trust drops below a threshold, policies automatically:

• Rotate keys
• Automate certificate management to renew, revoke, or update digital certificates
• Restrict or revoke access
• Quarantine devices
• Trigger remediation workflows

This makes identity not just an onboarding mechanism but a dynamic control plane.

AI and Zero Trust: Making Continuous Verification Possible

Zero Trust architecture requires organisations to “never trust, always verify”. In IoT and OT, this means:

• Every device must prove identity continuously
• Every interaction must be authenticated
• Every action must align with policy
• Behaviour must be monitored in real time
• Identity and access management must be implemented as a core component to enable continuous visibility, trust, and control across complex device ecosystems

But verification at scale is impossible without automation. AI enables Zero Trust to work not just in theory, but in actual, high-volume device ecosystems. AI-driven security measures are necessary for effective enforcement, providing advanced, scalable, and automated strategies to protect IoT devices and networks from evolving threats.

AI provides:

• Automated device identity verification
• Real-time anomaly detection
• Adaptive policy enforcement
• Predictive risk analytics

These AI-powered approaches strengthen IoT security by improving threat detection, anomaly identification, and adaptive access controls, ensuring Zero Trust principles are maintained at scale.

Behavioural baselining

Continually learning what “normal” looks like for each device class or behaviour pattern, AI-driven systems can then identify unusual behavior that may indicate anomalies or potential security threats.

Real-time anomaly detection

Spotting deviations that human analysts would never detect — including subtle timing changes, unexpected communications, unusual protocol use, suspicious lateral movement, or abnormal network traffic patterns.

AI-driven systems excel at monitoring network traffic in real time, enabling rapid detection of anomalies and potential threats as they emerge.

Effective anomaly detection is a critical component of network security, helping organizations identify and respond to cyber threats before they can cause significant harm.

Risk-aware authentication

Allowing trusted devices to operate freely while tightening controls for those showing uncertainty. Unlike traditional authentication methods designed for human users, risk-aware authentication for devices must account for unique factors such as device identity, operational context, and automated credential management. This distinction is critical, as devices require continuous, automated validation processes that differ significantly from the one-time or session-based authentication typically used for human users.

Policy adjustment based on predicted risk

If AI anticipates a device is heading toward a risky state — due to known vulnerabilities, firmware drift, abnormal patterns, or through predictive analytics forecasting device states — policy tightens before compromise occurs. These predictions and automated policy adjustments are driven by advanced AI algorithms and machine learning algorithms that analyze device behavior and environmental signals.

Zero Trust becomes proactive rather than reactive.

AI for IoT Threat Detection: Accelerating Response to Complex Attacks

IoT and OT attacks rarely look like conventional IT intrusions. They can involve:

• Rogue firmware updates
• Spoofed device identities
• Manipulated telemetry
• Malicious bootloader changes
• Supply chain tampering
• Lateral movement via ignored devices
• Multi-device botnets (e.g. Eleven11Bot)
• Emerging threats and evolving threats that leverage new vulnerabilities or attack vectors

AI is uniquely suited to identifying these threats because it can leverage advanced data analysis on the vast data generated by IoT devices, process real-time data streams, and analyze iot data to detect threats and identify potential threats quickly and accurately.

Analyse vast telemetry streams at machine speed

Traditional systems cannot process millions of events per second. By leveraging sophisticated AI algorithms, modern solutions can analyze vast telemetry streams in real time, enabling automated threat detection, anomaly identification, and continuous trust management across complex IoT environments.

Correlate data across device types

AI tools identify patterns that cut across medical devices, industrial sensors, and building controls by correlating data from multiple sources.

Spot slow-moving, subtle attacks

Many IoT breaches build gradually over weeks or months. AI systems provide ongoing surveillance, using machine learning and anomaly detection to monitor for subtle, slow-moving attacks. This continuous, automated approach helps identify unusual patterns and respond to advanced cyber threats in real-time across IoT environments.

Detect supply chain compromise

By analysing firmware behaviour, AI can flag malicious or tampered components, leveraging threat intelligence to identify known attack patterns and emerging threats.

What emerges is a security posture that is sensory, adaptive and continuous — a stark contrast to periodic, manually triggered assessments.

AI and Automated Device Discovery: Crucial for Unmanaged Assets

One of the biggest challenges highlighted in Device Authority’s July newsletter is the risk posed by unmanaged devices, which contribute to a third of all breaches and can lead to significant security breaches if left undetected.

AI improves discovery by:

• Identifying unknown devices based on network behaviour
• Recognising devices from incomplete metadata
• Classifying devices even if vendor information is missing
• Mapping communication patterns to detect shadow IoT
• Predicting device type using observed behaviour

This turns the Discovery Tool into an intelligent system that not only finds devices, but understands what they are, even when manufacturers provide minimal visibility.

AI Across the Device Lifecycle: Securing Onboarding, Operation, and Decommissioning

Managing the entire lifecycle of IoT devices is one of the most significant challenges facing enterprises today. With millions of connected devices entering, operating within, and eventually leaving critical infrastructure, every stage of the device lifecycle presents unique security risks. Traditional security methods, reliant on manual effort and static controls, simply cannot keep pace with the scale and complexity of modern IoT networks.

AI-powered automation is now essential for securing machine identities and managing access across the full device lifecycle—ensuring data integrity, regulatory compliance, and operational efficiency from onboarding to decommissioning.

Why AI Is Now Essential for Regulatory Compliance

Regulatory frameworks such as NIST, CRA and EO 14028 demand:

• Real-time visibility
• Identity-based access control
• Continuous monitoring
• Provable lifecycle management
• Immediate vulnerability response

These regulations are designed to help organizations prevent data breaches in IoT environments by ensuring robust security controls and proactive risk management.

AI enables organisations to meet these obligations by:

• Automatically mapping devices to compliance controls
• Detecting when devices fall out of compliance
• Generating auditable evidence of policy enforcement
• Applying automated remediation to restore compliance

Without AI, compliance becomes a slow, manual and unreliable process. With AI, compliance becomes continuous and provable.

KeyScaler 2025: Practical AI for Real-World IoT Security

KeyScaler 2025 integrates AI not as a bolt-on, but as a core part of the platform. Device Authority highlights several AI-driven upgrades:

• Automated identity lifecycle management
• Intelligent risk scoring
• Automated certificate and credential enforcement
• Real-time anomaly detection
• Behaviour-based policy workflows
• Agentless discovery enhanced by AI-driven classification
• Predictive maintenance through AI-powered analytics to forecast device failures and enable proactive interventions

In complex environments such as healthcare, energy, manufacturing and automotive, this automation saves enormous operational time while strengthening security.

Implementing AI-driven machine identity management with KeyScaler 2025 delivers significant benefits, including enhanced security, prevention of unauthorized access, and reduction of cyber threats—transforming an organization’s security posture.

AI makes KeyScaler 2025 not just a machine identity platform, but a machine trust platform.

What CISOs Should Do Next

CISOs planning their 2025 strategy should consider three immediate steps:

First, prioritize the protection of sensitive data generated and transmitted by IoT and OT devices, ensuring robust safeguards against cyber attacks, data breaches, and unauthorized access.

Second, implement automated device identity and credential lifecycle management to maintain security and compliance at scale.

Third, adopt zero-trust architectures and policy-based access control to secure complex, large-scale IoT ecosystems.

1. Establish AI-driven visibility

Start with an AI-supported discovery scan to identify unmanaged and unknown devices.

2. Shift from static certificates to dynamic identity

Adopt a machine identity platform capable of AI risk scoring and continuous trust assessment.

3. Integrate AI into Zero Trust enforcement

Policies should tighten, relax or revoke access automatically based on real-time behaviour.

These steps ensure IoT/OT ecosystems remain secure even as environments grow more complex.

Conclusion: AI Is No Longer Optional — It’s the Foundation of IoT Security

AI has moved from theoretical security enhancement to the practical backbone of modern IoT and OT protection.
In 2025, machine identity, Zero Trust and regulatory compliance all depend on automation driven by intelligent analysis.

As cyber threats accelerate and device ecosystems expand, organisations that adopt AI-driven identity and visibility platforms — like KeyScaler 2025 — will be able to secure their environments with confidence, clarity and control.

Those who rely on manual processes will fall behind, both technologically and in compliance terms.

The future of IoT security is automated, intelligent and identity-first — and it has already begun.