Integrating Security Alarm Installation into a Zero-Trust Model: Turning Physical Security into a Source of Telemetry for the Security Operations Centre (SOC)

In the ever-evolving landscape of cybersecurity, the boundaries between physical and digital security are becoming increasingly blurred.

Traditional security alarm systems, which were once considered basic perimeter defences designed to detect intrusions and alert personnel, are now becoming essential components of an integrated information security framework through modern Security Alarm Installation practices.

This change is being driven by the principles of Zero-Trust architecture, which state that no entity should be trusted by default and that continuous verification is essential.

Integrating physical security systems into the Security Operations Centre (SOC) allows organisations to use sensor telemetry to detect and respond to threats.

This convergence allows for network segmentation and treats sensor events as inputs for security information and event management (SIEM) systems, as well as security orchestration, automation and response (SOAR) systems.

It also reduces false positives through machine learning filters and addresses privacy concerns. As we move towards 2025, this integration will be essential for maintaining a resilient defence posture.

Risks in 2025: Convergence of physical and information security; attacks via IoT sensors.

The year 2025 marks a pivotal point in the convergence of physical security and information security (infosec), driven by the growing number of Internet of Things (IoT) devices.

Security alarms comprising sensors such as motion detectors, door contacts and cameras are becoming increasingly connected, which makes them vulnerable to cyber-physical attacks. Adversaries exploit these as entry points, blending kinetic and digital tactics.

Consider the risks: IoT sensors in alarm systems often operate on legacy protocols or insecure networks, which makes them vulnerable to exploitation.

For example, attackers could exploit firmware vulnerabilities in a wireless sensor to infiltrate broader networks.

Reports from cybersecurity firms highlight a surge in such incidents: in 2024 alone, there was a 30% rise in IoT-related breaches, with physical security devices implicated in 15% of cases involving critical infrastructure.

This convergence amplifies threats mapped to the MITRE ATT&CK framework. Initial access (TA0001) can be achieved through physical means, such as tampering with sensors to gain unauthorised entry.

This is followed by discovery (TA0007), whereby compromised devices scan internal networks. Ransomware groups have evolved to target these systems, either disabling alarms to facilitate physical intrusion or using them for data exfiltration.

In a zero-trust model, unintegrated physical security represents a blind spot that allows attackers to bypass digital controls.

This risk is further compounded by vulnerabilities in the supply chain, where malicious actors can embed backdoors in sensor hardware during the manufacturing process.

Organisations must recognise that ignoring this integration invites hybrid attacks, whereby a simple sensor hack can escalate to enterprise-wide compromise.

Architecture: Event broker, log normalisation and mapping to MITRE ATT&CK.

A robust architecture for integrating security alarms into a zero-trust model begins with an event broker — a centralised middleware that aggregates and routes telemetry from physical sensors.

Often implemented via tools such as Apache Kafka or MQTT protocols, this broker ensures the real-time streaming of events without overwhelming the network.

The next critical step is log normalisation. Sensor data arrives in various formats, such as JSON from IP cameras, syslog from hubs and proprietary protocols from legacy alarms.

Normalisation involves parsing these into a standardised schema, such as the Common Event Format (CEF) or the Elastic Common Schema (ECS), to facilitate ingestion into SIEM platforms.

This step enables correlation across domains: for example, a trigger from a door sensor might align with network logs showing anomalous traffic.

Mapping events to the MITRE ATT&CK framework enhances this telemetry. For physical vectors, events such as unauthorised door access could be categorised as ‘Initial Access via External Remote Services’ (T1133) if linked to a compromised badge reader, for example.

Discovery tactics are evident when sensors detect unusual movement patterns, which could indicate reconnaissance.

Enriching logs with ATT&CK tactics, techniques and procedures (TTPs) provides SOC analysts with contextual insights.

For instance, an alert from a motion sensor in a restricted area, when correlated with badge scans, could indicate Lateral Movement (TA0008).

This architecture transforms alarms into a telemetry source that feeds ML models in SIEM for predictive analytics.

Network layer: VLAN/zero-trust profiles for hubs and sensors, mutual TLS, key rotation

At the network layer, segmentation is fundamental. Virtual Local Area Networks (VLANs) isolate alarm components, with sensors on one VLAN, the central hub on a second VLAN, and integration points with Security Operations Centre (SOC) tools on a third VLAN.

This prevents lateral movement, meaning that a compromised sensor cannot access enterprise resources directly.

Zero-trust profiles enforce least-privilege access. Each device — whether a hub or a sensor — receives a unique identity that is verified via mutual Transport Layer Security (mTLS).

mTLS ensures bidirectional authentication, meaning that both the sensors and the hub authenticate to the SIEM endpoint.

Certificates are managed via a public key infrastructure (PKI) with automated issuance and revocation.

Key rotation increases resilience. Using short-lived certificates (valid for 24–48 hours, for example) mitigates the risk of key compromise.

Tools such as HashiCorp Vault can automate this process, rotating keys without causing any downtime.

Network policies enforced via software-defined networking (SDN) can be adjusted dynamically based on context — for example, scrutiny can be increased during off-hours.

This layer ensures that physical security devices adhere to the tenets of Zero Trust: verify explicitly, assume breach and use the least privilege necessary.

Without this layer, sensors become weak links that are susceptible to man-in-the-middle attacks or spoofing.

When planning the installation of a security alarm, it is important to immediately incorporate event export to SIEM and access segmentation according to Zero-Trust policies; otherwise, the sensors will remain ‘silent’ for the SOC.

Taking this proactive approach turns potential vulnerabilities into strengths by aligning physical assets with digital defences.

Incident Management: Correlating door, movement, video frame and access card data with SOAR playbooks.

Effective incident management hinges on correlating disparate signals. In an integrated setup, for example, a door sensor alert is combined with motion detection, video frames from cameras and access card logs to provide a comprehensive overview.

This multi-source correlation, powered by SIEM rules, identifies true positives; for example, a door breach without a valid card scan would trigger an alert.

SOAR playbooks automate responses. Upon correlation, a playbook may isolate the affected VLAN, notify responders via integrated communication tools and initiate forensic capture from video feeds.

Advanced playbooks incorporate machine learning (ML) for anomaly detection, flagging potential brute-force attacks such as repeated failed access attempts.

This process reduces the mean time to respond (MTTR). In 2025, manual triage will be untenable in the face of AI-driven threats; automation will ensure scalability.

For example, if correlation reveals a physical intrusion linked to network probes, the playbook could implement adaptive controls such as revoking temporary access tokens. Ultimately, this transforms physical telemetry into a multiplier of SOC efficiency.

False alarms: Combining PIR, microwave and CV triggers; quality metrics (FPR/TPR).

False alarms erode trust and waste resources. Modern integration systems mitigate this issue by combining different types of sensors: Passive infrared (PIR) sensors detect heat, microwave sensors detect motion via Doppler shifts and computer vision (CV) sensors analyse images.

Combining these technologies — for example, by requiring confirmation from both PIR and CV — reduces errors caused by environmental factors such as wind or animals.

ML filters can further refine this process. Trained on historical data, the models classify events and suppress benign triggers. Quality metrics guide optimisation.

The False Positive Rate (FPR) measures erroneous alerts, while the True Positive Rate (TPR) assesses detection accuracy. The aim is to achieve an FPR below 5% and a TPR above 95% through iterative tuning.

In practice, edge computing involves sensors processing data locally and only forwarding validated events.

This reduces both false alarms and bandwidth usage. Tracking metrics via dashboards ensures continuous improvement, establishing alarm systems as reliable sources of telemetry.

Privacy and compliance: Minimisation of PII, storage and terms, and auditing.

Privacy is paramount in integrated systems that handle sensitive data, such as video or access logs.

Minimise personally identifiable information (PII) by anonymising feeds — for example, by blurring faces in CV analysis or tokenising badge IDs.

Storage policies dictate retention periods in order to comply with regulations such as GDPR and CCPA.

Data is retained for 30–90 days unless it is required for a longer period for investigations. Data should be encrypted at rest and in transit, and access logs should be audited for compliance.

Audits involve regular reviews, including penetration testing of integrations, log reviews to detect unauthorised access and third-party assessments.

These measures ensure the ethical use of telemetry and prevent its misuse while maintaining information security efficacy.

Practical Checklist for Procurement and Implementation

To operationalize this integration without endorsing specific brands, follow this checklist:

· Assessment Phase: Evaluate current alarm infrastructure for IoT compatibility and Zero-Trust readiness. Identify sensors supporting standard protocols (e.g., MQTT, REST APIs) for event export.

· Procurement Criteria: Select systems with built-in mTLS support, event normalization capabilities, and ML for false positive reduction. Ensure hardware allows firmware updates and key rotation.

· Network Design: Implement VLAN segmentation and Zero-Trust profiles. Test mTLS configurations in a lab environment.

· Integration Testing: Set up event brokers and normalize logs for SIEM ingestion. Map sample events to MITRE ATT&CK and simulate correlations.

· Incident Playbooks: Develop SOAR automations for common scenarios, incorporating multi-sensor fusion.

· Privacy Controls: Configure PII minimization, define retention policies, and schedule audits.

· Deployment and Monitoring: Roll out in phases, monitor FPR/TPR metrics, and iterate based on feedback.

This checklist facilitates a smooth transition by establishing physical security as an essential source of SOC telemetry.

In conclusion, integrating security alarms into Zero-Trust frameworks transforms their function from that of peripheral hardware to that of dynamic information security assets.

By addressing risks, designing robust systems and prioritising privacy, organisations can protect themselves against hybrid threats in 2025.

This evolution requires foresight in order to turn potential weaknesses into strategic advantages.