Alvian Networks

National Institute of Standards and Technology (NIST) released the initial public draft of SP 800-82 Rev. 4, Guide to Operational Technology Security, on September 21, with authors from NIST and MITRE. Comments are open until November 30. The revision is organized around CSF 2.0 and expands its coverage of OT environments, including water and wastewater, building automation, agriculture, rail, maritime and IIoT/cloud environments. That broader treatment of OT security is relevant to a lot of the work we are doing at Alvian Networks around resilient networks, system integrity and trustworthy operational data. One section we are looking at closely is time synchronization. NIST addresses authenticated NTP, secure PTP, GNSS interference and spoofing, multiple time sources, and monitoring of the systems providing time. We plan to submit comments on one area we think deserves more attention: verifying the time reference itself. Authenticating NTP or PTP traffic helps protect the path between systems. It does not tell you whether the GNSS-derived reference feeding that path is actually correct. If the upstream reference is wrong, a secured timing network can still distribute the wrong time. If you operate OT in utilities, telecom, government or other critical infrastructure, the draft is worth reviewing and NIST is actively asking for operational feedback. Draft and comment template: https://lnkd.in/esZtCU6k⁠ Comments are due November 30, 2026 at sp800-82rev4@nist.gov #OTSecurity #NIST #Timing #CriticalInfrastructure

Sompo Group Japans's new marine cyber coverage is a good example of how GNSS interference can create a real financial loss without causing any physical damage. A vessel can be delayed, detained or unable to operate because its navigation systems are being affected by radio interference. If that turns into an insurance claim, there may be no damaged hull, failed component or broken machinery to inspect afterward. Much of the claim may come down to the records: when the interference started, what the systems were seeing, how long it lasted and which systems were affected. The operator needs to support the loss. The insurer needs confidence in the records being used to support it. That is where independently validated conditions, trustworthy timestamps, provenance and tamper-evident records become useful. The same issue applies well beyond shipping. Utilities, telecom networks, transportation systems and other critical infrastructure increasingly rely on digital records to reconstruct events and establish what actually happened. #GNSS #CriticalInfrastructure #DefensibleEvidence

In July, nearly 4,000 MW of data-centre load in Northern Virginia disconnected from the PJM grid following a transmission system fault. The facilities transferred to backup generation, leaving grid operators to manage a sudden loss of load along with swings in voltage and system frequency. PJM says it was the third measurable event of this kind in the last two years. On September 8, PJM proposed new ride-through requirements for large computational loads, including data centres. The intent is to keep these facilities connected through normally cleared voltage and frequency disturbances rather than having several gigawatts disconnect at once. A large data centre can now carry enough load to materially affect system conditions when it disconnects, and that changes how grid operators have to treat it. It also creates a harder problem after the event. The July disconnection happened in two waves, and the second was triggered by voltage changes the first one caused. The utility, the data centres, the backup generation and the network infrastructure may each hold records of different parts of what happened. Reconstructing the sequence means correlating those records across systems and locations, having enough confidence in the timestamps to put them in the right order, and preserving defensible evidence of what each system actually observed. #DataCenters #GridReliability #AlvianTrustStack

In July, a time server in Telstra’s mobile network restarted with a date 20 years in the past. The server was online, responding to queries and distributing time that downstream systems trusted. That outage is a useful reminder that synchronization and trustworthy time are not quite the same thing. Our latest article looks at the Telstra incident, the 2016 GPS timing anomaly, what the power industry learned after the 2003 blackout, and why ongoing timing monitoring still deserves more attention across critical infrastructure. When Time Breaks https://lnkd.in/g9YFD73f #CriticalInfrastructure #NetworkResilience #Timing #GNSS #Telecommunications

Our CEO, Ryan McCann, and Director of Strategic Partnerships, Joe Caruso, kicked off the exhibition at Big Data & AI Paris today as part of the Ontario delegation. The day brought a full schedule of conversations around AI, critical infrastructure, resilient networks and trusted operational data, along with opportunities to connect with companies from across Europe. Great to have Alvian Networks represented in Paris and looking forward to another full day tomorrow. #BigDataAIParis #CriticalInfrastructure #ArtificialIntelligence #AlvianNetworks

Proud to be part of the inaugural ARC project within Hamilton’s new Critical Infrastructure Protection Cluster. The collaboration brings together complementary Canadian expertise across network resilience, operational assurance, trusted timing, sensing, validation and critical infrastructure protection for municipalities. For Alvian, it is a natural extension of the work we are already doing to help critical infrastructure providers build, strengthen and operate more trustworthy, resilient systems. #CriticalInfrastructure #InfrastructureResilience #Municipalities.

Alvian Networks is hiring a Systems Developer to join our core engineering team in Dundas, Ontario. This involves hands-on work on the Alvian Edge Node (Patent Pending), our hardware platform for critical infrastructure providers. You will report directly to our CTO, building and integrating units, developing acquisition and validation software, and testing the platform as it grows. Hybrid role, regular on-site time required for hardware work. Details and how to apply are in the job post below. https://lnkd.in/diJd4SKU

The recent attention around Flock Safety’s automated license plate readers is a useful reminder of how quickly a machine observation can become an assumed fact. There is no doubt these systems can be useful for law enforcement. For example, a camera captures a vehicle, software reads the plate and other characteristics, and that information can give police a lead within seconds. Flock itself, however, notes that plate translations can sometimes be incomplete or inaccurate and that users should confirm the computer-generated result before taking action. That verification step is what matters on multiple levels and its not always happening. There have now been documented cases where incorrect plate reads or other errors in the process contributed to innocent motorists being stopped, detained at gunpoint or arrested. The issue also extends well beyond license plates into all facets of society where we have machines and AI making these types of assertions. A camera can capture an image, and AI software can tell you what it thinks is in that image. The resulting record may include a timestamp, location and device information, but each of those is still an assertion made by a system. Increasingly, those assertions are being used by people and automated systems to make real decisions and sometimes with real consequences. For that record to become defensible evidence, you need to know more. Which device created it? When and where was the observation actually made? Were the timing and positioning references trustworthy at that moment? Has anything changed since the record was created? Even then, a trustworthy record does not make an incorrect AI classification correct. What it can do, though, is establish exactly what the system observed and asserted, when and where it happened, and whether the record remained intact afterward. That becomes important when someone later has to understand why a decision was made. This problem will grow as municipalities and critical infrastructure operators deploy more cameras, sensors and AI into everyday operations. More decisions will begin with something a machine observed, classified or inferred. That is one of the problems we built the Alvian Trust Stack to address. It independently validates time and location, establishes device and event provenance, cryptographically seals records, and preserves a defensible history of what happened. Calling something a source of truth sets a high bar and as more decisions start with machine-generated observations, the evidence behind them needs to meet it. #CriticalInfrastructure #DigitalEvidence #DataIntegrity #AI

Poland’s National Institute of Telecommunications published GNSS monitoring data from its Gdańsk station last week, and the scale is hard to ignore. In May and June, the station recorded significant multi-hour interference across all GNSS bands on 70% of days. Completely interference-free days accounted for only 10% of May and 13% of June. During the first 19 days of August, complete disruption was recorded on 63% of days. The Institute has been monitoring anomalies since January 2024 and says the situation deteriorated sharply this year. At those levels, GNSS disruption becomes part of the operating environment. Systems relying on satellite navigation or timing have to be designed around the possibility that the reference may disappear, degrade or behave unexpectedly on a regular basis. The visible effects are easier to recognize. The Institute says mobile maps can lose signal completely, show the wrong location or fail to calculate a route. It also warns about effects on shared transportation systems and drones. Critical infrastructure also has another exposure that gets less attention, the widespread use of GNSS for timing across telecom, utilities, transportation, broadcast and other infrastructure. Some systems will alarm when the reference degrades. Others may enter holdover and continue operating. Depending on the design and monitoring in place, the loss of the external reference may never become an obvious operational event. For the operator, that raises some practical questions. What happened to the reference? When did it happen? How long did it last? How did the system respond, and how trustworthy was the time being distributed during that period? Poland can put numbers around the problem because the National Institute of Telecommunications has a station in Gdańsk continuously measuring the GNSS environment. Without that kind of visibility, operators may have far less information about what their systems were actually exposed to. GNSS interference is becoming part of the operating environment. Wherever critical infrastructure depends on GNSS, operators need to know when the reference changes, how their systems respond, whether the time and data they continue to rely on remain trustworthy, and have a defensible record of what happened when they do not. #GNSS #TimingIntegrity #CriticalInfrastructure #InfrastructureResilience

A real-world example of why validating time matters across critical infrastructure.