PNT Assurance & Anti‑spoofing: Making GNSS Trustworthy for Critical Operations

When timing and navigation underpin safety, trading, protection, or mission systems, GNSS can’t be a black box. You need continuous assurance that your positioning, navigation, and timing (PNT) is authentic, within tolerance, and operating under known risk. This post outlines a practical assurance stack detection, analytics, response, and testing and how our brand partners fit in to deliver verifiable outcomes.

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Why PNT assurance now?

Global Navigation Satellite Systems have become the invisible backbone of modern infrastructure. From financial trading floors that require microsecond-accurate timestamps to power grids that synchronise protection relays across hundreds of kilometres, GNSS provides the timing reference that keeps critical systems coordinated. But this ubiquity has created a dangerous dependency. When GNSS fails or is deliberately attacked, the consequences cascade rapidly through interconnected systems.
Spoofing attacks have evolved far beyond the crude replay techniques of the past. Modern spoofing is targeted, coherent, and increasingly difficult to detect with conventional receivers. An attacker can gradually shift your receiver’s perception of time or position without triggering obvious alarms, steering systems off course while appearing to maintain signal lock. Timing drift of even a few microseconds propagates into timestamped logs, control loops, financial trades, protection relays, and forensic records. What starts as a small timing error becomes a large operational incident, and in many cases, organisations don’t realise they’ve been compromised until well after the damage is done.
Most organisations monitor GNSS uptime and basic signal quality, but few have the capability to detect sophisticated attacks or prove integrity during an audit. Regulators and auditors are increasingly asking hard questions: How do you know your timing source hasn’t been compromised? What happens to your services when GNSS degrades? Can you distinguish between natural interference and deliberate attack? Without dedicated detection, analytics, and failover mechanisms, these questions have no satisfactory answer.
Effective PNT assurance requires a layered approach that combines edge detection, centralised analytics, resilient timing infrastructure, and regular testing. You need sensors at the antenna to identify threats in real time, analytics to correlate events across sites, hardened clocks that maintain service during outages, and simulation tools that let you prove your defences work before an incident occurs. This isn’t about adding one piece of equipment—it’s about building an integrated assurance system that detects, responds, and maintains service through the full spectrum of GNSS threats.

An assurance architecture that holds up under stress

Sense and detect at the edge
Threats begin at the antenna. By the time a spoofing or jamming attack reaches your timing server or receiver, it may be too late to respond effectively. Traditional GNSS receivers are designed to acquire and track signals, not to question their authenticity. They’ll happily lock onto a spoofed signal if it’s stronger or more coherent than the genuine satellite constellation. This creates a fundamental vulnerability: the device that provides your timing reference has no inherent ability to validate that reference.
Effective threat detection requires dedicated sensors positioned at the point of vulnerability. The RF front end continuously analyse signal characteristics for anomalies. These sensors must look beyond basic signal strength to examine constellation geometry, Doppler shifts, carrier-to-noise ratios, multipath signatures, and statistical patterns that indicate interference or manipulation. A genuine GNSS signal from satellites in medium Earth orbit exhibits specific physical characteristics; deviations from these norms can reveal the presence of terrestrial interference or spoofing attempts.
We deploy multi-band GNSS integrity probes at antennas, rooftops, and equipment racks using GPSPatron sensors equipped with on-device classifiers. These probes don’t just raise an alarm when signal quality drops. They classify the type of threat, measure its severity, and provide the forensic data needed to understand what’s happening and why. When a probe detects anomalous constellation geometry combined with sudden carrier phase shifts, it classifies the event as probable spoofing and raises an alert with confidence metrics. This classification happens in real time at the edge, giving operators actionable intelligence within seconds of threat emergence.

Correlate and analyse centrally
A single sensor at one site can detect local interference, but it can’t tell you whether you’re experiencing a site-specific issue, a regional problem, or a coordinated attack across multiple locations. An antenna cable fault looks different from a city-wide jamming event, which looks different from a targeted spoofing attempt against your organisation. Without correlation across geography, time, and sensor type, you’re drowning in isolated alerts and struggling to separate signal from noise.
Effective threat characterisation requires streaming RF metrics and anomaly events from edge sensors to a centralised analytics platform that can correlate patterns. Is the interference affecting all your sites in a city, or just one antenna? Did it start gradually or appear suddenly? Are multiple GNSS constellations degraded simultaneously, or just GPS? Are nearby organisations experiencing similar effects? These patterns tell you whether you’re dealing with equipment failure, environmental interference, or deliberate attack, and they inform your response strategy.
GPSPatron GP-Cloud receives telemetry from distributed sensors and applies correlation algorithms to distinguish local interference from regional or systemic threats. It learns your normal operating environment—typical signal strengths, constellation visibility, multipath patterns—and generates alerts when patterns deviate beyond learned thresholds. Instead of reacting to individual sensor alarms that may or may not indicate real threats, your security operations centre receives high-confidence alerts with context: “Multiple sites in Sydney CBD experiencing coherent spoofing attempt beginning 14:23 AEDT, confidence 94 per cent, attack vector characterised as ground-based simulator bearing 280 degrees from Site A.” This context enables decisive response rather than lengthy investigation.

Protect the timing core
Even with perfect detection, you still need timing services to remain within SLA while GNSS is degraded or under attack. If your grandmaster clock blindly follows a spoofed GNSS signal, or simply stops distributing time when GNSS quality drops, downstream systems fail immediately. Trading platforms halt, protection relays lose coordination, and timestamped logs become unreliable. Detection without resilient failover merely tells you that you’re going offline—it doesn’t keep you operational.
Your authoritative timing source must have the intelligence to detect GNSS anomalies, the holdover capability to maintain accuracy during outages, and the policy flexibility to fail over to trusted alternative references like PTP or NTP. A hardened grandmaster doesn’t just receive time—it validates multiple sources, arbitrates between conflicting references, and disciplines high-stability oscillators to ensure continuous service even when all external references become suspect. This requires more than a simple GNSS receiver and a clock; it requires a timing appliance designed for critical infrastructure.
Meinberg grandmaster clocks provide this resilient architecture. High-stability OCXO or rubidium oscillators deliver extended holdover performance—maintaining microsecond-class accuracy for hours or days when GNSS is unavailable. Multi-GNSS receivers (GPS, GLONASS, Galileo, BeiDou) cross-validate constellation data, making coherent spoofing attempts more difficult because an attacker must simultaneously spoof multiple independent systems. Policy-based reference selection enables sophisticated failover logic: if GNSS integrity is questioned by GPSPatron sensors, the grandmaster can automatically isolate the compromised GNSS input and maintain NTP or PTP distribution from holdover or alternative references. Services stay within SLA while your team investigates and resolves the threat.

Keep transport deterministic
When timing must traverse complex networks between data halls, substations, or secure enclaves, transport latency and path variability can erode timing accuracy. You might have a grandmaster delivering perfect microsecond-class timestamps, but if those timestamps traverse congested Ethernet switches where timing packets queue behind bulk data traffic, endpoints receive timing with variable delay that destroys synchronisation. Even with Precision Time Protocol, standard network infrastructure doesn’t provide the guarantees that critical timing applications require.
Critical timing applications demand deterministic transport—bounded latency, predictable paths, and seamless failover when links degrade. You need time-aware networking that treats timing packets as a first-class service with reserved bandwidth and priority queuing, ensuring that timing quality delivered by your grandmaster is preserved all the way to the endpoint. In multi-site deployments, this often means engineering dedicated timing domains with redundant paths and automatic switchover.
Relyum platforms deliver this deterministic transport using Time-Sensitive Networking (TSN), High-availability Seamless Redundancy (HSR), and Parallel Redundancy Protocol (PRP). TSN reserves bandwidth for timing packets and enforces bounded latency through time-aware scheduling, ensuring that timing frames traverse the network with minimal jitter. HSR and PRP provide seamless redundancy by transmitting duplicate frames on parallel network paths—when the primary path fails, the secondary path is already carrying valid timing data, eliminating switchover delay entirely. This architecture maintains timing integrity across distributed networks even during link failures or network maintenance, ensuring that endpoints remain synchronised within tolerance through all operating conditions.

Test, train, and accredit
You can’t prove your PNT assurance architecture works by waiting for an attack. When an incident occurs in production, it’s too late to discover that your sensors don’t detect certain spoofing profiles, or that your failover policy has an edge case, or that your security team doesn’t know how to interpret alerts and execute response playbooks. Assurance requires evidence, and evidence requires testing under controlled conditions before threats emerge in the field.
Regulators and auditors increasingly want proof that your defences have been tested against realistic threat scenarios. Your blue team needs training on how to recognise and respond to GNSS attacks. Your engineers need characterisation data to tune detection thresholds and validate failover behaviour. None of this can happen in production without risking service disruption, which means you need a lab environment that can reproduce authentic GNSS signals, inject realistic interference profiles, and generate coherent spoofing attacks on demand.
Teleplan Forsberg GNSS and RF simulators provide this controlled test environment. They generate realistic satellite constellations with accurate signal characteristics, then inject jamming and spoofing scenarios that let you test your entire assurance stack without touching production systems. You can simulate a progressive jamming attack and verify that sensors detect and classify it correctly, that grandmasters fail over within acceptable time, and that timing remains within tolerance throughout the event. You can run blue-team drills that train security personnel on incident response procedures, capturing metrics on detection time, alert interpretation, and response execution. The forensic data and performance metrics generated during these tests become the evidence base for compliance documentation and accreditation, demonstrating to auditors that your PNT assurance isn’t theoretical—it’s been validated under realistic threat conditions.

Typical use cases

Critical infrastructure timing: maintaining service during attack
A national power grid operator relies on GNSS-disciplined clocks at substations to synchronise protection relays and timestamp event records across thousands of kilometres of transmission lines. Timing errors of more than one millisecond can cause protection coordination failures, potentially leading to cascading outages or equipment damage. The operator becomes aware that GNSS spoofing attacks are increasing in sophistication and frequency globally, and regulators are asking pointed questions about PNT resilience. The operator currently has no capability to detect spoofing attacks in progress or to maintain timing service during GNSS impairment beyond brief receiver holdover periods measured in minutes.

The organisation needs edge detection at each substation antenna to identify spoofing attempts in real time, coupled with grandmaster clocks that can fail over gracefully to extended holdover when GNSS is compromised. Security operations teams need alerts with sufficient context to distinguish real threats from false positives, and they need response playbooks that maintain grid timing coordination during investigations. The solution must work across a geographically distributed network where substations are connected via wide-area communication links with varying reliability.
Deploy GPSPatron sensors at rooftop antennas across the substation network, positioned to provide both local threat detection and network-wide triangulation of interference sources. Meinberg grandmasters with rubidium oscillators replace aging GNSS receivers, providing 24-hour holdover capability and multi-constellation diversity. Grandmaster failover policies are configured to isolate GNSS inputs when GPSPatron raises high-confidence spoofing alerts, maintaining timing distribution from rubidium holdover while the SOC investigates. GP-Cloud telemetry integrates with the security operations centre so that SOC analysts receive correlated alerts with severity ratings, affected areas, and recommended responses.
The result is verifiable resilience: when a spoofing attempt is detected at any site, the grandmaster isolates GNSS and maintains timing from holdover while the SOC investigates and facilities teams inspect RF environments. The protection system remains coordinated and operational throughout. Regular testing with Teleplan Forsberg simulation validates detection thresholds and failover timing, generating compliance evidence for regulatory review.

Finance and data centres: audibility and compliance
A financial services firm operates high-frequency trading systems where microsecond-accurate timestamps prove trade priority during market disputes and regulatory investigations. Recent regulatory guidance suggests that firms must demonstrate timing source integrity and resilience—merely asserting that “we use GPS” is no longer sufficient. Auditors are asking how the firm can prove that timing sources have not been compromised, whether timing would remain accurate during a GNSS outage, and what monitoring is in place to detect attacks in progress.
The firm needs verifiable integrity evidence, continuous monitoring of GNSS health with tamper-evident logs that can be produced during investigations, combined with hardened timing infrastructure that demonstrates resilience during impairment. Audit reports must show that spoofing and jamming would be detected before they affect trading timestamps, and that timing services would remain within regulatory tolerance (typically single-digit microseconds) during GNSS unavailability lasting hours or days.
Install GPSPatron probes on trading floor timing systems and configure continuous telemetry export to long-term storage with cryptographic integrity verification. This creates a tamper-evident record of GNSS health that can be correlated with trading activity during investigations. Meinberg grandmasters with disciplined rubidium holdover replace conventional GNSS receivers, and authenticated PTP distribution prevents network-based timing attacks. Failover policies ensure that if GNSS integrity is questioned, the system maintains accurate time from holdover while alternative references are validated.
During quarterly audits, the firm generates reports showing GNSS health metrics, alert history, holdover test results, and timing accuracy measurements across all material time periods. Compliance teams use Teleplan Forsberg simulation to demonstrate detection thresholds and failover behaviour in controlled lab conditions, providing documentation that satisfies regulatory scrutiny. When market disputes arise, the firm can produce forensic timing logs that prove timestamp integrity with cryptographic evidence, giving legal teams defensible evidence for arbitration.

Defence and public safety: resilience in contested environments
A defence organisation operates secure enclaves that rely on GNSS for positioning and timing in operational environments where adversaries may deliberately contest the RF spectrum. Communication systems, situational awareness platforms, and weapons systems all depend on accurate PNT to function effectively. The organisation needs resilient PNT that continues to deliver usable position and timing during jamming or spoofing, and it must validate these capabilities during operational test and evaluation before deploying systems to theatre.
Resilience in contested environments requires multi-layered defence that goes beyond commercial critical infrastructure. Detection at the antenna provides early warning, but systems must continue to operate through sustained RF denial. Failover mechanisms must maintain service without operator intervention, because personnel may be managing other priorities during contact. Network segmentation must ensure that a compromised timing source in one domain doesn’t propagate to other systems. And all of this must be validated under realistic threat conditions that match intelligence estimates of adversary capabilities.
We deploy GPSPatron sensors at forward antennas and secure facilities to provide early warning of RF threats, with sensor data routed to tactical operations centres for real-time situational awareness. Timing enclaves are segmented using Relyum boundary clocks and HSR networks, ensuring that a compromised timing source in one domain can’t affect other systems. Meinberg grandmasters with extended rubidium holdover and multi-source reference diversity provide continued timing service during sustained GNSS denial, with policy-driven failover that requires no operator intervention.
Operational test and evaluation uses Teleplan Forsberg to replay threat scenarios derived from intelligence assessments and theatre observations. Engineers validate that systems detect threats within specified timeframes, that failover executes correctly under stress, and that positioning and timing remain within mission tolerances during extended RF denial. Test results feed into operational risk assessments and deployment decisions, giving commanders verifiable evidence of system resilience before units deploy to contested environments.

Implementation playbook

1. Threat model and coverage
Begin by mapping your GNSS infrastructure comprehensively: every antenna, cable run, receiver, timing server, and downstream consumer. Identify single points of failure where a compromised antenna or receiver could affect multiple systems. Assess your RF environment—are you in an urban canyon with multipath issues, near an airport with potential interference, or in a region where deliberate jamming or spoofing is a credible threat? Map your critical timing consumers and their accuracy requirements: does the trading floor need microseconds, or can the building access control system tolerate milliseconds?
Use this threat model to determine sensor placement priorities. Position GPSPatron probes to maximise detection probability and enable triangulation of interference sources. Cover high-value antennas first—those supporting critical systems or protecting multiple consumers—then expand to secondary sites as budget allows. If you have multiple antennas on a single roof farm, place at least one probe per antenna to detect localised spoofing attempts that might target specific receivers. Document your coverage map and any gaps that remain for future expansion.

2. Clocking and failover policy
Commission Meinberg grandmaster clocks with holdover specifications that match your service-level requirements. If you need one-microsecond accuracy for 24 hours during GNSS outage, specify rubidium oscillators. If you can tolerate ten microseconds for 8 hours, OCXO may suffice at lower cost. Configure multi-source reference selection policies that define which GNSS constellations to trust, when to fail over to PTP or NTP, and what conditions trigger holdover mode. If you have multiple grandmasters across sites, establish a reference hierarchy and cross-validation rules.
Document failover thresholds and test them regularly. What signal quality metrics trigger warnings versus alarms? At what confidence level do GPSPatron spoofing alerts cause GNSS isolation? How long can you maintain service in holdover before accuracy degrades beyond SLA? These parameters should be documented, validated in testing, and reviewed after any incident. Configure authenticated NTP and PTP where protocol support allows, preventing network-based timing attacks that bypass GNSS security measures.

3. Network path quality
Engineer deterministic timing domains across your network infrastructure. Identify critical timing paths between grandmasters and endpoints, and ensure these paths have reserved bandwidth and bounded latency guarantees. In complex networks with multiple switches and routers, this may require dedicated VLANs, quality-of-service policies, or separate physical timing networks. Measure your timing paths under load to verify that latency remains bounded and symmetric even during peak traffic.
Apply Relyum TSN switching where microsecond-class synchronisation must traverse complex or high-traffic networks, using time-aware scheduling to guarantee that timing packets are never queued behind bulk data. Use HSR or PRP where seamless redundancy is required—these protocols eliminate switchover time entirely by transmitting duplicate frames on parallel paths, making link failures invisible to timing consumers. Validate path quality with continuous monitoring and periodic test measurements using precision timing instrumentation, verifying that end-to-end accuracy meets requirements under all operating conditions.

4. Analytics and alert routing
Integrate GPSPatron GP-Cloud with your security operations centre, network operations centre, and SIEM platform using standard APIs or syslog. Define alert severity levels and routing policies that match your operational procedures: low-severity interference alerts might page facilities teams to inspect antennas during business hours, whereas high-confidence spoofing alerts trigger immediate grandmaster failover and wake SOC analysts at any hour. Configure alert context to include location, affected systems, threat classification, and confidence metrics so that responders have actionable intelligence immediately.
Establish response playbooks for each alert type with clear roles, actions, escalation paths, and success criteria. Who gets paged for each severity level? What immediate actions do they take? When do you isolate GNSS inputs versus continuing to monitor? When do you notify customers, regulators, or law enforcement? Document these playbooks and train relevant personnel on execution. Configure auto-actions where appropriate—for instance, automatically isolating a compromised GNSS input when spoofing confidence exceeds 90 per cent, or triggering antenna inspections when signal quality degrades below thresholds.

5. Prove it in lab
Before deploying your assurance architecture to production, validate the entire stack in a controlled lab environment. Use Teleplan Forsberg GNSS simulators to reproduce jamming, spoofing, antenna faults, and cable impairments under repeatable conditions. Verify that sensors detect and classify each threat type with acceptable accuracy and false-positive rates. Confirm that grandmasters fail over within specified timeframes and that timing remains within tolerance throughout events. Test your monitoring dashboards and alert routing to ensure that operators receive the information they need to respond effectively.
Run blue-team drills that train security and operations personnel on incident response procedures. Inject simulated threats while teams execute playbooks, measuring response times and identifying gaps in procedures or training. Capture quantitative evidence—detection thresholds, failover times, holdover accuracy, alert latency—to support accreditation and compliance documentation. Tune thresholds and policies based on lab results before go-live, and repeat validation testing after any significant configuration changes or software updates. Maintain a test schedule that ensures your assurance capabilities remain validated as threats evolve.

Response playbooks (examples)

Suspected spoofing at Site A
At 14:23 AEDT on a Tuesday afternoon, the GPSPatron sensor at Site A rooftop antenna detects anomalous constellation geometry combined with sudden carrier phase shifts. The on-device classifier analyses the signal characteristics and determines this matches a coherent spoofing profile with 92 per cent confidence. The sensor raises a HIGH severity alert to GP-Cloud, including RF signature data, affected GNSS constellations, estimated attack vector, and sensor location.
GP-Cloud correlates the Site A alert with telemetry from twelve other metropolitan sensors and determines that neighbouring sites show normal signal characteristics, suggesting a localised attack rather than regional interference. The platform generates a correlated alert: “Probable spoofing attack at Site A, confidence 92%, localised within 500-metre radius, GPS and GLONASS affected, originated 14:23:17 AEDT.” This alert routes to the security operations centre via SIEM integration and triggers an API call to the Site A Meinberg grandmaster.
The grandmaster receives the spoofing alert and executes its configured response policy. It pins the internal rubidium oscillator to holdover mode, immediately drops the compromised GNSS input from its reference selection algorithm, and fails over to a trusted PTP reference sourced from Site B grandmaster 15 kilometres away. NTP and PTP services distributed from the Site A grandmaster continue without interruption, now disciplined by rubidium holdover and the remote PTP source. Downstream systems experience no service disruption. The grandmaster logs the failover event with timestamps and reference quality metrics for post-incident review.
SOC analysts receive the correlated GP-Cloud alert on their monitoring dashboard and via SMS page. They open the incident ticket, review the RF signature and confidence metrics, and confirm that Site A has failed over to holdover as expected. Following the documented playbook, they block any new GNSS-dependent device onboarding at Site A until the threat is cleared, and they dispatch facilities personnel to physically inspect the rooftop antenna and surrounding RF environment for suspicious equipment or vehicles.
Facilities arrives at Site A within 40 minutes and conducts a visual inspection of the roof farm and neighbouring buildings. They identify no obvious sources but document the inspection with timestamped photos. The GP-Cloud telemetry continues to show spoofing signatures for another 15 minutes, then signals return to normal characteristics at 15:12 AEDT. The SOC monitors for recurrence over the next two hours, then authorises the Site A grandmaster to resume using GNSS references at 17:30 AEDT, with continued elevated monitoring.
Post-incident, the engineering team exports the captured RF signature from GPSPatron sensors and replays it in the lab using Teleplan Forsberg simulation. They validate that detection thresholds performed as designed and that failover timing met SLA requirements. The incident report includes complete forensic data, alert timeline, failover metrics, and grandmaster holdover accuracy measurements for compliance records. The security team briefs executive leadership on the event and response, noting that services remained within SLA throughout and that detection and failover automation worked as designed.

Progressive jamming near the roof farm
At 09:15 AEDT on a Thursday morning, GPSPatron sensors on a multi-antenna roof farm begin detecting gradual carrier-to-noise ratio (C/N0) decline across all visible GNSS constellations. Initial degradation is minor, 2 to 3 dB below baseline and the sensors raise a WARN severity alert indicating possible interference. Network operations centre staff receive the alert but take no immediate action, as WARN-level alerts typically indicate transient environmental conditions that resolve naturally.
Over the next 20 minutes, C/N0 continues declining. By 09:35 AEDT, signal levels have dropped 8 to 10 dB below baseline and constellation visibility is reduced. The GPSPatron sensors cross their ALARM threshold and escalate to HIGH severity, classifying the event as probable jamming based on the gradual onset, broadband characteristics affecting all constellations, and lack of spoofing indicators. GP-Cloud correlates sensor data and confirms that all six antennas on the roof farm show identical degradation patterns, suggesting a single interference source affecting the entire site rather than individual equipment failures.
The automated playbook triggers notifications to the facilities team via SMS and email: “HIGH: Probable jamming at Main Campus Roof Farm, all antennas affected, C/N0 degraded 8-10 dB, investigate RF environment for new interference sources.” Meinberg grandmasters at the site detect the signal quality degradation through their own receivers and automatically increase the weighting on rubidium oscillator holdover while continuing to track the degraded GNSS signals. Timing services remain within SLA as the grandmasters blend GNSS and holdover references according to their disciplining algorithms.
Facilities responds to the roof farm and conducts a visual inspection of the immediate area. They note that construction has begun on an adjacent building overnight, with new cellular equipment being installed on a tower approximately 50 metres from the roof farm. The timing aligns with the onset of interference. Facilities contacts the building management and confirms that a telecommunications provider installed a new 5G base station, which was activated for testing around 09:00 AEDT that morning.
The facilities team coordinates with the telecommunications provider to temporarily reduce the base station power while they investigate the interference. Within ten minutes of power reduction, GPSPatron sensors show C/N0 recovering toward baseline levels. The facilities team and telco engineers work together over the next two hours to adjust the base station antenna orientation, verify filter installation, and validate that spurious emissions are within regulatory limits. By 12:00 AEDT, GNSS signal quality has returned to normal and the sensors clear their ALARM status.
Throughout the event, Meinberg grandmasters maintained timing services within single-digit microseconds of UTC by blending degraded GNSS references with rubidium holdover. Downstream systems experienced no timing disruptions. The facilities team logs the incident with complete RF spectrum data captured by GPSPatron sensors, coordinates with the telco to implement permanent filtering and antenna adjustments, and schedules follow-up monitoring to verify the issue doesn’t recur. The engineering team updates the site documentation to note the new base station as a potential interference source for future reference.

What “good” looks like

Authenticity

Your detection system distinguishes between spoofing, jamming, multipath, equipment faults, and benign anomalies with high confidence and low false-positive rates. Alerts arrive with classification tags, severity ratings, affected constellations, estimated threat vectors, and forensic RF data that gives operators immediate context. Security teams trust the alerts enough to execute response procedures without spending hours investigating whether the threat is real. False positives occur rarely enough that alert fatigue doesn’t erode response discipline, but detection sensitivity remains high enough that real threats don’t go unnoticed.

Operators have visibility into GNSS health across all sites through unified dashboards that show constellation availability, signal quality trends, anomaly events, and threat classifications in real time. Historical data lets analysts identify patterns—recurring interference sources, equipment degradation trends, or attack attempts that probe defences. Forensic RF captures can be exported for detailed analysis or replayed in lab environments to validate detection thresholds and response procedures.

Availability

Timing and navigation services remain within defined tolerances during GNSS impairment thanks to robust holdover, reference diversity, and automated failover. When GNSS quality degrades or spoofing is detected, grandmaster clocks seamlessly transition to internal oscillators or alternative references without service interruption. Downstream systems continue operating normally while your team investigates and resolves the threat. Holdover performance is quantified and validated—you know precisely how long you can maintain accuracy during extended GNSS denial because you’ve tested it under controlled conditions.

Timing path redundancy ensures that network failures don’t become timing failures. Critical endpoints receive timing via diverse physical paths with automatic switchover, making infrastructure failures transparent to applications. You’ve engineered deterministic timing domains with bounded latency and reserved bandwidth so that timing packets traverse networks with predictable delay regardless of bulk traffic load. End-to-end timing quality is continuously monitored with alerting when path performance degrades below thresholds.

Observability

Comprehensive dashboards show timing offset trends, constellation health, signal quality metrics, alert history, failover events, and policy state across all sites. Operators can drill down from network-wide views to individual sensor telemetry, grandmaster reference selection details, or timing path measurements. Dashboards clearly distinguish between normal operations, degraded-but-operational states, and alarm conditions requiring response. Alert context includes remediation guidance so that first responders know what actions to take.

Timing quality data and assurance telemetry export to long-term storage with tamper-evident integrity, creating audit trails that support compliance and forensic investigations. When auditors ask about timing source integrity during a specific period, you can produce comprehensive evidence: signal quality metrics, constellation availability, alert events, failover actions, and accuracy measurements with cryptographic verification that the records haven’t been altered. This observability transforms PNT from a black box into a verifiable service with evidence-based assurance.

How iTkey helps

We design and deliver PNT assurance as an integrated system, not a collection of isolated products. The challenge isn’t finding individual components—it’s architecting those components into a coherent system that detects threats, maintains service, and produces evidence across diverse operational environments. That requires deep expertise in GNSS vulnerabilities, timing infrastructure, network engineering, security operations, and regulatory compliance.

We start by understanding your threat environment, timing requirements, and operational constraints. Where are your GNSS antennas, and what threatens them? What timing accuracy do your applications require, and for how long must you maintain that accuracy during GNSS denial? How do your security operations currently detect and respond to RF threats, and how will PNT assurance integrate with existing workflows? These questions shape the architecture we design.

GPSPatron sensors and GP-Cloud analytics provide early warning and threat characterisation, detecting spoofing and jamming before they affect services. Meinberg grandmasters deliver resilient timing infrastructure that maintains accuracy through GNSS impairment using disciplined holdover and reference diversity. Relyum network platforms preserve timing quality across complex distribution paths with deterministic transport and seamless redundancy. Teleplan Forsberg simulation validates your entire assurance stack under controlled conditions, generating the evidence that proves your defences work. Together, these components deliver trusted PNT with verifiable resilience and compliance-grade observability.

We deliver this as a turnkey service: threat assessment, reference architecture, equipment commissioning, policy configuration, integration with security operations, personnel training, and ongoing support. You get a complete PNT assurance system with documented procedures, validated performance, and evidence-based confidence that your timing and navigation will remain trustworthy through the full spectrum of GNSS threats.

Ready to strengthen your PNT resilience?

Share your antenna map, critical systems, and compliance targets with us. We’ll return a reference design showing sensor placement, grandmaster specifications, failover policies, and integration points with your security operations. The design will include a bill of materials with equipment specifications and costs, and a pilot plan that demonstrates measurable value within weeks rather than months.

If you’re facing regulatory questions about PNT integrity, preparing for compliance audits, or responding to emerging threats in your operational environment, we can help you build verifiable assurance that satisfies auditors and maintains service through GNSS impairment.

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