Tag: post-quantum cryptography

  • In VIAVI’s Survey, a 2.7x Detection Edge Ties AI-Era Security to the Org Chart

    In VIAVI’s Survey, a 2.7x Detection Edge Ties AI-Era Security to the Org Chart

    TL;DR · 30-second read

    The Short Version

    A yearly survey of 750 corporate technology workers in seven countries, run for the testing-equipment maker VIAVI, found that more companies are merging two teams that used to work apart: the people who keep the company network running, and the people who defend it from hackers.

    Companies that merged the teams were nearly three times as likely to say they now spot attacks much faster.

    That matters as attacks increasingly use artificial intelligence. Still, about one company in five has no plan to merge at all.

    VIAVI Solutions said in a release distributed by PR Newswire on October 21, 2026, that its 2027 State of the Network study found 37% of surveyed organizations have fully converged their network and security operations, up from 27% a year earlier. Another 45% said they are partway through the change. That puts 82% either finished with or working toward what the industry calls NetSecOps, a single operating model for the teams that run networks and the teams that defend them.

    This is the study’s 18th edition. Research firm Omdia conducted it, surveying 750 networking and security staff in Brazil, Canada, France, Germany, Mexico, the UK and the US. Respondents ranged from IT leadership to management and came from organizations of many sizes and industries. Organizations that had converged were 2.7 times as likely to report a significant improvement in mean time to detect (MTTD), the average time it takes to notice a fault or threat: 35% did, against 13% of the rest.

    Executive Summary

    The headline finding is about how organizations are structured, not about a product. Network operations (NetOps) teams watch traffic to keep it fast and available. Security operations (SecOps) teams watch the same traffic for threats. The two have often used separate tools and separate data. VIAVI’s study says that split is closing quickly. Completed convergence rose ten percentage points in a year, and only about a fifth of respondents have neither finished nor started the transition.

    The study connects converged teams with better self-reported detection. It also names structured testing as the most common way organizations avoid trading security for performance. That means checking technologies such as post-quantum cryptography, AI security tools, SASE and cloud networking before they go live. For organizations whose networks carry AI applications or face AI-assisted attackers, the findings suggest security outcomes depend partly on how teams are organized and how changes are tested, not only on which tools are bought.

    Two caveats apply. The data is self-reported survey data. It was published by a company whose business includes test and measurement, network performance and threat-detection products. And the release presents correlations, not controlled comparisons. The findings are best read as strong directional signals rather than proof.

    One Number, Two Meanings: Reading the 37%

    The figure 37% appears twice in the study, measuring two different things. In 2026, 37% of respondents reported fully converged network and security operations. The rise from 27% is ten percentage points, and as a relative increase that is also about 37%, because 10 divided by 27 is roughly 0.37. So the headline’s 37% increase and the body’s 37% of organizations happen to land on the same number. Both are accurate. Anyone comparing this with other surveys should be clear about which one they are quoting.

    The full breakdown is 37% complete, 45% in progress and about 18% neither. The largest group is the one still mid-transition. That group is where demand for integrated tools, redesigned processes and cross-trained staff is concentrated, which matters to suppliers, systems integrators and hiring managers. In practice, convergence usually means shared visibility into traffic, shared data and joint workflows, so that a slowdown and a possible intrusion are looked at together instead of being passed between departments.

    The 2.7x Detection Edge Points to Structure, Not Just Software

    The study’s most striking result compares respondents with and without a NetSecOps strategy. Among those with one, 35% reported a significant improvement in mean time to detect. Among those without, 13% did, a ratio of about 2.7. Detection speed matters because the longer a fault or intruder goes unnoticed, the more time it has to cause damage. Among organizations that had completed the switch, 44% cited enhanced security as a key benefit, 30% saw faster detection, 22% reported fewer incidents and 22% said investigations took less time.

    The likely reason runs through the traffic both teams already share. A performance slowdown can be the first sign of an attack. A new security control can itself cause a slowdown. When two teams use separate tools, each sees half the picture, and every handoff between them adds delay. Jim Frey, principal analyst at Enterprise Strategy Group from Informa TechTarget, said in the release that attack surfaces are expanding with increasingly sophisticated and AI-enabled threats, so NetOps and SecOps need shared visibility. Chris Labac, who leads VIAVI’s network performance and threat business, called aligning the organizational model with integrated monitoring and security platforms a critical success factor. Both point to the same lever. The variable behind the 2.7x figure is an operating model, which is why this reads as an org-chart finding rather than a product finding.

    The limits deserve equal weight. The comparison is correlational. Organizations that finish convergence may already be larger, better funded or more mature, and those traits could drive better detection on their own. A significant improvement is a self-assessment, not a measured result. The benefit figures are also modest: fewer than a quarter of completers reported fewer incidents. The defensible conclusion is that convergence is associated with faster detection. Security and network leaders facing AI-era threats should therefore treat team structure as a lever alongside tooling, but merging teams does not guarantee results.

    Testing Before Turning It On

    The study’s second theme is structured testing. More than half of respondents cited pre-deployment validation of new technologies as their most common approach. Examples include post-quantum cryptography (encryption designed to resist future quantum computers), AI security, SASE (secure access service edge, which delivers networking and security controls as a cloud service) and cloud networking. Among respondents that test, 52% reported fewer outages and service disruptions, 44% fewer or less severe security incidents, and 42% better application performance or latency.

    The problem behind this is that security work slows networks down. Inspecting, encrypting and filtering traffic all use processing capacity and can add delay. Most organizations use several countermeasures at once. Sixty-five percent use AI and machine-learning tools to tune security and performance together. Another 65% do capacity planning so security measures do not strain the network. Fifty-eight percent test network and security controls before deployment, and 58% test and tune them continuously afterward. AI therefore shows up on both sides of the problem: it is a source of new threats and a tool for managing the trade-offs.

    VIAVI’s core business is test and measurement, so findings that favor testing line up with what it sells. That does not make the findings wrong, but it makes question wording and denominators more important than usual. The figures also do not fully line up. Pre-deployment validation is described as cited by more than half of respondents, while pre-deployment testing of network and security controls is put at 58%. These appear to come from different questions, and the two are not reconciled.

    The Fifth That Hasn’t Started

    About 18% of respondents report no completed or in-progress convergence. Frey framed the challenge as reaching these organizations and showing them the benefits. If the study’s correlation holds, these organizations sit in the group where only 13% reported a significant detection improvement, at a time when Frey describes threats as increasingly AI-enabled. For suppliers, the 45% in progress is the near-term market and the 18% is a longer-term persuasion effort. For the organizations themselves, the first step the study points to is organizational and does not require a large purchase: shared visibility between two teams that already watch the same traffic.

    Background

    VIAVI Solutions, based in Chandler, Arizona, and listed on Nasdaq as VIAV, describes itself as a global leader in test and measurement and optical technologies. Its products test and secure data center ecosystems and communication networks. It also supplies resilient positioning, navigation and timing systems for military, aerospace, railway and first-responder communications, plus optical technologies for anti-counterfeiting, consumer electronics, industrial and automotive markets.

    State of the Network, now in its 18th edition, is VIAVI’s annual survey of network performance and security priorities; Omdia conducted this year’s research. The convergence it tracks addresses a familiar split in corporate IT. Network operations teams focus on uptime and speed, and security operations teams focus on threats, yet the two often work from separate tools and data while monitoring the same traffic.

    Sources

    Source: VIAVI State of the Network Study: NetSecOps Deployments Increase 37% YoY, but One in Five Enterprises Still Lack a Transition Plan, VIAVI Solutions’ October 21, 2026 announcement of its Omdia-conducted survey of 750 networking and security professionals.

  • Huawei’s 98% Link-Use Claim Pitches the AI Network as a Way to Win Back GPU Time

    Huawei’s 98% Link-Use Claim Pitches the AI Network as a Way to Win Back GPU Time

    TL;DR · 30-second read

    The Short Version

    Huawei, the Chinese technology company, showed European customers an upgraded line of networking equipment in Florence this week.

    Its main pitch: the cables and switching boxes that link the chips training artificial intelligence often leave those chips waiting in traffic. Huawei says its new design keeps 98% of the connections busy, makes the computing more than 7% faster, and needs 30% less equipment.

    It is also selling security built into the hardware, at a time when European buyers are rethinking how much they rely on Chinese suppliers. The performance figures are Huawei’s own.

    Huawei unveiled an upgraded Stellar Intelligent Network portfolio for Europe at the Huawei Network Summit 2026 Europe in Florence on October 17, 2026, issuing the announcement in Italian, Dutch, Turkish, Slovak and Russian. The update covers three product lines: Stellar Intelligent Fabric for data center networks, Stellar Intelligent WAN for links between sites, and Stellar Intelligent Campus Network for offices and branches. It also adds new eKit networking products for small and mid-sized businesses.

    Huawei said its self-developed “100T” switches enable a two-tier, multi-plane data center architecture that quadruples network capacity while using 30% fewer switches and optical modules, and that its Network Packet Load Balancing (NPLB) algorithm lifts link utilization to 98% and AI training and inference performance by more than 7%. The same week, Huawei pressed the same AI-era networking message in Vienna. It announced a network-security partnership with Macau operator CTM at Network X 2026. At UBBF 2026 on October 13, it highlighted large-scale commercial use of its NG WAN solution, where data communication product line vice president John Cai called AI the decisive opportunity for networks.

    Executive Summary

    The Stellar upgrade is a broad enterprise networking refresh, but its most consequential claim concerns AI data centers. Huawei is arguing that the network connecting AI accelerators is not passive plumbing. In its account, the network determines how much useful work expensive compute actually does, and a better-balanced fabric recovers performance that would otherwise be lost to congestion.

    The second thread is security. Nearly every product line in the upgrade carries a built-in security feature: an intrusion-detection card, an integrated quantum key distribution card, post-quantum cryptography across wired and wireless links, and threat detection running directly on switches. Huawei is presenting security as a property of the hardware rather than an add-on. That is a pointed choice in a European market where buyers have been reassessing their exposure to Chinese network suppliers.

    For buyers, the announcement is a set of specific, testable numbers rather than vague promises. However, none of the figures comes with a stated baseline, cluster size or independent benchmark. The useful response is to treat them as a procurement checklist, not a conclusion.

    The Network as a Compute Multiplier

    The headline claim rests on a mechanism that AI cluster operators know well. Large training jobs split work across many GPUs or other accelerators, which must constantly exchange intermediate results in lockstep. When those exchanges hit a congested link, every chip in the job waits for the slowest transfer. Conventional data center load balancing assigns whole traffic flows to paths, typically by hashing. That approach can pile several large flows onto the same link while neighbouring links sit underused. Huawei’s NPLB algorithm, as its name indicates, balances at the level of individual packets, spreading traffic across all available paths. Huawei says this drives network-wide link utilization to 98% and raises training and inference performance by more than 7%.

    If that figure holds in a customer’s environment, the economics are straightforward. A 7% throughput gain on a fixed cluster is roughly equivalent to adding 7% more compute without buying more accelerators, power or floor space. Accelerators are the dominant cost in an AI cluster, so recovered utilization is worth far more than the network’s own price tag. That is the commercial logic Huawei is selling: spend on the fabric to get more out of the chips.

    The claim needs context before it can be priced. Huawei has not said what the 7% is measured against, on which models, at what cluster scale, or with which accelerators and network adapters. Packet-level spraying also typically requires endpoints that can handle packets arriving out of order, so the gain may depend on the rest of the stack. Operators should treat 98% and 7% as hypotheses to reproduce on their own workloads.

    Fewer Tiers, Fewer Optics

    The fabric’s other claim targets hardware count. Huawei says its self-developed 100T switches support a two-tier, multi-plane architecture that quadruples network capacity while cutting the number of switches and optical modules by 30%. In industry usage, the 100T label denotes switching capacity on the order of 100 terabits per second per device. Higher-capacity switches let a network reach the same number of servers with fewer layers.

    That matters because optical modules — the transceivers that convert electrical signals to light at each end of a fibre link — are a large and growing share of AI network cost and power. Every extra tier between servers adds more links, more transceivers and more hops. A flatter design reduces all three, which is why Huawei frames the 30% reduction as a total cost of ownership saving. The affected parties are clear: cluster builders weighing capex, and optics suppliers whose unit volumes shrink when topologies flatten.

    As with the performance claim, the comparison point is unstated. Quadrupled capacity and 30% fewer components relative to which previous design, at what scale, are the numbers a network architect would need. Huawei describes a three-level energy-saving framework covering devices, network and operations aimed at European environmental and carbon requirements, but it attached no energy figures to it.

    Security Built Into the Box, Sold Into a Cautious Market

    Security runs through every line of the upgrade. On the WAN side, Huawei cites an intrinsic security card that it says detects and blocks intrusions in real time with over 95% precision. It also cites what it calls the first integrated quantum key distribution (QKD) card, which uses quantum physics to exchange encryption keys and which Huawei says cuts deployment cost by more than 60%, plus APN6-based isolation of sensitive data. On campus networks, Huawei offers post-quantum cryptography (PQC), meaning encryption designed to resist future quantum computers, across wired and wireless connections. It also says local inference on switches cuts threat-detection time from 5 minutes to under 10 seconds, and that more than 20 inline algorithms in branch gateways detect 95% of unknown threats. For distributed AI, Huawei says layered model partitioning keeps raw data on premises.

    Embedding security in network hardware has real operational appeal. It can mean fewer separate appliances, fewer integration points and faster response at the edge. It also concentrates trust in a single supplier. For any vendor, European buyers increasingly ask who can verify the security functions, which cryptographic standards they implement, and whether independent certification is available. That question carries particular weight for Huawei, given the scrutiny Chinese network suppliers have faced in Europe. Huawei’s Vienna announcements point the same way: the CTM partnership is framed around strengthening network security in the AI era. The company is making security its lead argument rather than a defensive footnote.

    WAN and Campus Claims Aimed at the Budget Line

    Beyond the data center, the claims target recurring costs. Huawei says its lossless Starnet algorithm delivers more than 95% cross-region compute efficiency and cuts dedicated line costs by 60%, an argument aimed at enterprises running AI workloads across distant sites. On campus networks, it claims an 84% reduction in wireless interference, double the industry-average performance, a 30%-plus cut in total cost of ownership for 10 Gbps campus networks via multi-GE switches and flexible Right-to-Use licensing, and mean time to repair under 5 minutes.

    The small-business lineup is more concrete. It comprises Aura10/AR580 converged gateways with antivirus and intrusion prevention, S630 10GE core switches that can manage up to 1,000 access points for hotels, and AP362H access points with 1.6x antenna gain for retail. These are product specifications rather than benchmark claims, and they show Huawei pushing the same security-first message down-market. Its NG WAN commercial momentum, presented at UBBF 2026, suggests the WAN story is being sold to operators and enterprises alike.

    Background

    Huawei is a Chinese technology company that supplies telecom operator equipment and enterprise networking gear. Stellar Intelligent Network is its umbrella for enterprise networking, spanning the data center fabric that links servers and AI accelerators, the wide-area networks that connect distant sites, and the campus networks inside offices, hotels and shops.

    AI data centers have made networking a first-order design problem. Training large models requires thousands of chips to exchange data constantly, so congestion, hardware count and optical transceiver costs now weigh on cluster economics alongside the chips themselves. Huawei presented the Stellar upgrade at its Florence summit days after related Vienna events, Network X 2026 and UBBF 2026, where it framed AI as the defining opportunity for network vendors and operators.

    Sources

    Source: Huawei presenta la soluzione Stellar Intelligent Network aggiornata per l’Europa, basata sulla filosofia della “connettività sicura e intelligente” — Huawei’s announcement of its upgraded Stellar fabric, WAN and campus networking portfolio for Europe, also issued in Dutch, Turkish, Slovak and Russian.

    Primary sources: Безопасность на основе искуственного интеллекта для Макао — CTM и Huawei заключают партнерское соглашение для усиления безопасности сети в эпоху ИИ (CTM and Huawei network-security partnership, Network X 2026, October 17, 2026); Решение Huawei NG WAN для крупномасштабного коммерческого использования стимулирует рост операторских сетей (Huawei NG WAN large-scale commercial use, UBBF 2026, October 15, 2026).

  • Keyfactor’s $1B+ Investment at $200M ARR Treats AI Machine Identity as Infrastructure

    Keyfactor’s $1B+ Investment at $200M ARR Treats AI Machine Identity as Infrastructure

    TL;DR · 30-second read

    The Short Version

    A security software company called Keyfactor just closed an investment of more than $1 billion, led by the investment firm Summit Partners. Keyfactor brings in about $200 million a year from subscriptions, so the investment is more than five times its yearly subscription income.

    Keyfactor makes the digital ID badges that servers, apps and AI assistants use to prove who they are to each other. As AI multiplies the number of machines talking to one another, those badges start to look like basic plumbing.

    It also added the former boss of CyberArk, a security firm sold for about $25 billion, to its board.

    Keyfactor, an Atlanta- and Stockholm-based maker of machine identity and cryptography software, said on Oct. 5, 2026 that it has appointed former CyberArk CEO Matt Cohen to its board of directors and as a strategic advisor to CEO Jordan Rackie. The company said the appointment coincides with the closing of its previously announced growth investment of more than $1 billion led by Summit Partners, and follows its recent milestone of surpassing $200 million in annual recurring revenue (ARR, the yearly value of its subscription contracts).

    Executive Summary

    The headline item is a board seat, but the more consequential fact is the money behind it. Keyfactor has closed a growth investment of more than $1 billion led by Summit Partners, at a point when the company reports just over $200 million in annual recurring revenue. The company says the capital will fund product development, international expansion, strategic acquisitions and investment across its global organization, with particular emphasis on its Trust Control Plane, a unified layer for managing machine identities and encryption.

    That ratio matters. Whatever the deal’s precise structure, a commitment of more than $1 billion is at least five times Keyfactor’s recurring revenue. Investors do not typically write checks of that size relative to revenue for a point tool; they write them for something they expect enterprises to treat as permanent infrastructure. Keyfactor’s own pitch is that AI agents, cloud workloads and the coming shift to quantum-resistant encryption make machine identity exactly that.

    Cohen’s appointment reinforces the scale-up message. He ran CyberArk, an identity security company acquired by Palo Alto Networks for roughly $25 billion, and earlier helped PTC grow past $1 billion in ARR. Keyfactor says it recruited him specifically for experience at the size it aspires to reach.

    The Check Is the Story, Not the Board Seat

    Board appointments are routine; a $1 billion-plus investment into a company with roughly $200 million in recurring revenue is not. Even without knowing the valuation, or how much of the money is new capital for the company versus payments to existing shareholders, the arithmetic sets a floor: the investment alone equals at least five years of Keyfactor’s current subscription revenue. That is a bet on durability and expansion, not on a single product cycle.

    The stated use of proceeds supports that reading. Keyfactor lists product innovation, international expansion, investment in its organization and, notably, strategic acquisitions. A company that plans to buy other companies is signalling an intent to consolidate a fragmented category rather than simply sell more of one product. For buyers, that usually means a vendor that wants to own more of the stack, with the roadmap changes and integration work that tend to follow acquisitions.

    Cohen’s role fits that plan. Rackie said Keyfactor pursued him because he has “operated at the scale we aspire to reach.” The release does not describe an exit, public listing or sale strategy, and readers should not infer one. What it does describe is a board being assembled for a much larger company than the one that exists today.

    Why Machine Identity Gets Treated as Infrastructure

    Every time two machines talk securely, whether a server and a database, a cloud workload and an API, or an AI agent and the system it acts on, each side typically proves its identity with a cryptographic credential such as a digital certificate. Public key infrastructure (PKI) is the system that issues, tracks and renews those credentials. When one expires unnoticed or is compromised, services can fail or become exposed. The mechanism Keyfactor is selling against is simple: the number of machine identities grows with every new workload, and Keyfactor argues AI agents are now adding to that population at an unprecedented rate.

    That growth is what turns certificate management from an administrative chore into infrastructure. Keyfactor describes enterprises as running fragmented tools across their cryptographic estate; its Trust Control Plane is pitched as a single layer that provides visibility, policy and automated action across all of it. Layered on top is post-quantum readiness: the expectation that much of today’s encryption must eventually be replaced with algorithms designed to resist future quantum computers. That migration requires an organization to know where all its cryptography lives before it can change it, which is an inventory problem at data-center scale.

    The people affected are the ones who run large fleets: cloud and data center operators, enterprise security teams, and the platform engineers deploying AI agents that need credentials of their own. For them, a heavily capitalized vendor in this layer is a mixed signal. It points to more investment in automation and post-quantum tooling, but also to a category consolidating around fewer, larger suppliers.

    What Is Claimed Versus What Is Shown

    The release substantiates three concrete facts: the investment exceeds $1 billion, it is led by Summit Partners and has closed, and Keyfactor’s ARR has passed $200 million. Those are meaningful, verifiable-in-kind numbers.

    Other claims are positioning rather than evidence. Keyfactor describes itself as “the leader in trust infrastructure for AI and machines,” but offers no market-share data, customer counts or growth rates to support the ranking. Likewise, the release ties demand to AI agents and post-quantum migration without saying how much current revenue comes from those uses versus long-standing certificate management. The AI framing may well prove accurate; on the disclosed numbers, it is a thesis the investment is funding rather than a result already in the books.

    Background

    Keyfactor, headquartered in Atlanta and Stockholm, sells software that helps enterprises and government agencies discover, issue and manage the digital certificates and encryption keys that machines use to authenticate one another. Historically this work, known as public key infrastructure or certificate lifecycle management, was handled by scattered tools and manual processes. As cloud computing, containerized software and now AI agents have multiplied the number of machines inside a typical enterprise, vendors like Keyfactor have repositioned it as a strategic security layer.

    The identity security market has seen large-scale consolidation, illustrated by Palo Alto Networks’ roughly $25 billion acquisition of CyberArk, the company Matt Cohen led. Keyfactor’s recent announcements, including passing $200 million in ARR and closing a Summit Partners-led investment of more than $1 billion, place it among the larger independent companies focused specifically on machine identity and cryptography.

    Sources

    Source: Keyfactor Appoints Former CyberArk CEO Matt Cohen to Board of Directors and Strategic Advisor, Keyfactor’s Oct. 5, 2026 announcement of the board appointment and the closing of its Summit Partners-led investment.

  • VIAVI’s 100G Tester Makes Post-Quantum Overhead on AI Traffic a Pre-Launch Number

    VIAVI’s 100G Tester Makes Post-Quantum Overhead on AI Traffic a Pre-Launch Number

    TL;DR · 30-second read

    The Short Version

    A new generation of encryption is coming, built to withstand future quantum computers that could one day crack today’s codes.

    The catch: stronger locks can mean more work for the equipment that handles every connection, including the chatbots and other artificial intelligence services businesses now run.

    VIAVI, a maker of network testing gear, released a pizza-box-sized machine that floods networks with realistic, quantum-safe encrypted traffic, so companies can find out whether the new locks slow things down before customers notice.

    VIAVI Solutions (NASDAQ: VIAV) introduced the CyberFlood CF50 on September 29, 2026, a one-rack-unit (1U) test appliance that generates realistic encrypted application traffic at up to 100 gigabits per second (100G) to test application performance, security controls, AI inference and large language model (LLM) services. VIAVI said the CF50 includes native support for post-quantum cryptography (PQC), delivers TLS performance 1.75 times that of its nearest competitor, and has the industry’s lowest power consumption in its class.

    The CF50 is aimed at network equipment manufacturers, systems integrators and service providers, and sits below the existing CyberFlood CF1000, which VIAVI targets at large enterprises that need the highest performance.

    Executive Summary

    The CF50 is a traffic generator: it impersonates large numbers of users and applications so that engineers can see how firewalls, gateways, load balancers and AI services behave under load before real customers arrive. What distinguishes it, by VIAVI’s account, is that it combines three things in a small box: Layer 4-7 traffic (traffic that looks like real applications rather than raw data packets), security assessment, and AI inference testing, with post-quantum encryption built in.

    That combination matters because two infrastructure transitions are happening at once. Organizations are putting AI inference, the step where a trained model answers requests, into production over encrypted connections, and they are preparing to move those connections to quantum-resistant encryption. Both place new demands on the same security equipment. A tester that can generate both kinds of traffic turns the question of whether post-quantum encryption slows AI services into something a lab can measure rather than something operations teams discover after launch.

    The headline performance claims, 1.75X TLS performance and lowest power consumption, are VIAVI’s own, and the company has not published the competitor, metric or test conditions behind them. The practical value of the launch rests less on those superlatives than on whether PQC and AI inference traffic can be exercised together at realistic scale.

    Post-Quantum Encryption Is a Performance Question, Not Just a Security One

    Post-quantum cryptography replaces the mathematical methods that protect today’s encrypted connections with algorithms designed to resist future quantum computers. The US standards body NIST published its first post-quantum standards in 2024, and the pressure to adopt them comes partly from the risk that adversaries are recording encrypted traffic now in order to decrypt it later. The trade-off is that post-quantum key exchanges use considerably larger keys than the methods they replace, which means more data and more computation each time an encrypted TLS session is set up. TLS is the protocol behind the padlock in a browser and behind most API traffic.

    That cost does not fall evenly. It lands on the devices that terminate or inspect encryption at scale: next-generation firewalls, secure web gateways, load balancers and the Zero Trust architectures (security designs that verify every connection rather than trusting anything inside the network perimeter) that VIAVI’s announcement names. AI inference traffic runs through exactly this equipment. A user query to an LLM-powered service is typically an encrypted request that passes security controls before it reaches the model and after the answer comes back, and inference services are judged heavily on responsiveness. Any added handshake overhead or reduced connection capacity shows up directly as latency or as a need for more security hardware.

    This is where the CF50 changes the workflow. VIAVI says the appliance generates encrypted and mixed application traffic up to 100G with native PQC support, and separately offers AI inference and LLM testing. For the network equipment makers, integrators and service providers VIAVI is targeting, that means the performance impact of switching a firewall or gateway to post-quantum TLS can be measured against realistic application and AI traffic in the lab, before deployment. The overhead becomes a number that goes into sizing and procurement decisions rather than a surprise in production. How fully that promise holds depends on whether both traffic types can run in the same test, which VIAVI’s announcement implies but does not spell out.

    What the 1.75X and Lowest-Power Claims Do and Do Not Establish

    VIAVI states that the CF50’s TLS performance is 1.75 times that of the nearest competitor and that it has the industry’s lowest power consumption. Both claims are plausible marketing for a product in a competitive test-and-measurement category, and both are unverified. The company has not named the competitor or product compared, specified whether the metric is new connections per second, concurrent sessions or encrypted throughput, stated whether the comparison used classical or post-quantum cipher suites, or published a wattage figure.

    Those distinctions matter to buyers. A tester that leads on encrypted throughput may not lead on handshake rate, and handshake rate is where post-quantum overhead concentrates. A power figure matters for labs with constrained rack power, but lowest-in-class means little without knowing the class. The quote from Mauricio Sanchez of Dell’Oro Group supports the general case for realistic pre-deployment testing of AI and encrypted workloads. It does not address the CF50’s specific performance figures, and readers should not treat it as independent validation of them.

    A Smaller Box Widens Who Can Afford This Kind of Testing

    High-scale encrypted traffic testing has traditionally required large chassis systems and specialist operators. The CF50 packs the capability into 1U, a single standard rack slot about 1.75 inches tall, and replaces a Windows desktop client with a web interface. VIAVI also includes automated goal-seeking, which ramps load until a device under test reaches its limit, MITRE ATT&CK frameworks, a public catalog of real-world attacker techniques used to check whether security controls detect them, and repeatable traffic-mix testing.

    The commercial logic is to extend enterprise-scale testing below the tier served by the CF1000 to smaller equipment vendors and integrators. If the portability and price hold up, more of the security and networking supply chain can test post-quantum and AI-traffic performance in-house rather than relying on vendor datasheets. VIAVI has not disclosed pricing, so the cost-effectiveness claim, like the performance claims, awaits specifics.

    Background

    VIAVI Solutions, based in Chandler, Arizona, is a test-and-measurement and optical technology company. Its test products are used to validate data center, communications network and security infrastructure, and it also supplies resilient positioning, navigation and timing systems for military, aerospace, railway and first-responder communications, alongside optical technologies for anti-counterfeiting, consumer electronics and automotive markets.

    CyberFlood is VIAVI’s line of application and security test platforms, which emulate large numbers of users and applications to measure how networks and security devices perform. Before the CF50, the line’s flagship was the CF1000 for large enterprises. The new model arrives as organizations move AI inference into production and begin preparing encrypted connections for the transition to post-quantum cryptography.

    Sources

    Source: VIAVI Announces Smallest, Lightest, Most Efficient Platform for 100G Enterprise-Scale Application, Security and AI Inference Testing, VIAVI Solutions’ September 29, 2026 announcement of the CyberFlood CF50 test appliance.

  • Quantum-GUARD’s One-Year Deadlines Put Grid Encryption on AI’s Power Risk List

    Quantum-GUARD’s One-Year Deadlines Put Grid Encryption on AI’s Power Risk List

    TL;DR · 30-second read

    The Short Version

    Two senators, one from each party, have proposed a law to prepare the power grid for quantum computers. These are a new kind of machine that could one day crack the digital locks protecting the grid’s control systems.

    The bill gives the Energy Department one year to study the danger and set up a test lab where power companies can try stronger locks.

    Why it matters: the huge computer warehouses that run artificial intelligence depend on the grid. If the grid’s security slips, their electricity is at risk too.

    Legis1 reported that Sens. Chris Coons (D-DE) and Mike Rounds (R-SD) introduced the Quantum Grid Utility Assurance and Resilient Defense Act, or Quantum-GUARD Act, on August 14, 2026. The bill targets a future threat: quantum computers powerful enough to break the encryption that protects the electric grid’s IT and control systems.

    The bill would require the Federal Energy Regulatory Commission (FERC) to weigh quantum risks when it reviews grid reliability standards. It would give the Department of Energy one year to stand up a post-quantum cryptography testing sandbox with utilities, grid operators and vendors, and one year to report to Congress on quantum risks to the bulk-power system. IonQ, American Binary, the Quantum Industry Coalition, the Quantum Economic Development Consortium and the Cyber Threat Alliance endorsed it.

    Executive Summary

    The Quantum-GUARD Act is the first bill aimed specifically at moving the electric grid onto post-quantum cryptography. These are encryption methods designed to withstand attacks from quantum computers. The bill does not order utilities to replace anything. It directs regulators to study the problem, build a joint testing environment and factor quantum risk into the rules that govern grid reliability. Sen. Rounds described it as codifying parts of President Trump’s June executive order on advanced cryptographic attacks, which set a federal policy of moving government systems to NIST-approved post-quantum standards and helping critical infrastructure owners do the same.

    For the data center industry, the significance lies in the dependency, not the quantum computers themselves. AI campuses are among the largest new customers of the grid, and their uptime rests on utility control systems they neither own nor secure. Encryption upgrades to those systems are slow to plan and deploy. A bill that starts one-year federal clocks on assessment and testing marks the point at which grid cryptography becomes something power-hungry operators should track alongside capacity, interconnection and price.

    Why Grid Encryption Belongs on the AI Power Risk List

    A data center’s power is only as dependable as the systems that run the grid behind it. Those systems include substation controls, generator dispatch and the communications between control rooms and field equipment, which engineers call operational technology, or OT. Much of that traffic is protected by public-key cryptography, the same family of math that quantum computers are expected to threaten. The Quantum-GUARD Act names this directly: it would require FERC to consider post-quantum cryptography in both IT and OT systems, and it tasks the Energy Department with identifying obstacles to moving ‘high-value systems’ to new standards.

    The one-year deadlines explain why operators should track this now rather than later. Under the bill, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response would have twelve months to launch a testing sandbox and twelve months to deliver findings to the Senate Energy and Natural Resources and House Energy and Commerce committees. Those are the first steps of a migration, not the end of one. Grid control equipment often stays in service far longer than office IT, so any cryptographic change moves through procurement, testing and field replacement over years. For a company planning an AI campus that depends on a particular utility for a decade or more, how that utility handles the transition is a real long-term risk, even though the bill never mentions data centers.

    In practice, this is a due-diligence item rather than an alarm. Large-load customers already question utilities about capacity, redundancy and restoration times. The bill’s framework makes it reasonable to add a question about cryptographic readiness, and to expect that the answer will be shaped by whatever the DOE study and sandbox produce.

    What the Bill Does, and What It Leaves Open

    The bill’s verbs matter. FERC would have to ‘consider’ quantum risk when reviewing proposed reliability standards. That makes quantum risk a factor in rulemaking but sets no deadline for migration. DOE would ‘study’ and ‘establish’ a sandbox, with no requirement that utilities adopt what it finds. This is a framework bill. It builds the evidence base that later mandates, if any, would rest on.

    That restraint has advantages. Utilities run a very wide mix of legacy and modern equipment, and a mandate issued before anyone has tested post-quantum algorithms on real grid hardware could cause more reliability problems than it solves. The sandbox exists to test that, bringing utilities, vendors, federal agencies and state and local organizations into one environment to find where new cryptography breaks old equipment. The cost is time. By design, the bill’s first concrete output is a report, and the harder questions of who pays, on what schedule and under what enforcement come after it.

    Why Now: The Standards Exist, the Threat Does Not Yet

    The timing follows the standards. NIST finalized its first three post-quantum cryptography standards in August 2024 and has encouraged organizations to begin transitioning, which means there is now a defined target to migrate toward. Before that, a grid-specific migration bill would have had nothing concrete to point at.

    The threat, by contrast, is still prospective. The bill is framed around protecting the grid ‘before sufficiently powerful quantum computers can undermine existing encryption,’ and it does not say when that might happen. That is the right way to frame it. The argument for starting early rests on how slowly large infrastructure changes its cryptography, not on any fixed date for when quantum machines will arrive. Readers should treat claims of an imminent quantum break, from any side, with the same caution the bill’s own wording shows.

    A Quantum Industry Coalition Behind a Grid Bill

    The endorsements come mainly from the quantum and cybersecurity sectors: IonQ, a quantum technology company; American Binary; the Quantum Industry Coalition; the Quantum Economic Development Consortium; and the Cyber Threat Alliance. Support from companies that build quantum and security technology is expected and does not weaken the bill’s reasoning, which rests on NIST’s published standards and an existing executive order. It does mean that the people who would carry out the migration, meaning utilities, grid operators and state regulators whose ratepayers ultimately fund utility upgrades, will shape how practical the plan turns out to be, and their public positions on the bill are worth watching.

    Background

    The US electric grid runs under mandatory reliability standards, including cybersecurity rules. These are largely developed by the North American Electric Reliability Corporation and approved by FERC. The Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) leads the department’s work on energy-sector security. Grid cybersecurity has historically focused on present-day threats such as intrusions and ransomware. Quantum computing adds a longer-horizon concern: that a sufficiently powerful machine could defeat the public-key encryption that protects much of the grid’s communications.

    NIST’s finalization of its first three post-quantum cryptography standards in August 2024 gave organizations a concrete target for migration. Federal policy has since moved toward requiring that transition for government systems and supporting it for critical infrastructure. Meanwhile, rapid data center construction, driven largely by AI, has made grid reliability a central concern for the digital infrastructure industry.

    Sources

    Source: Bipartisan Bill Targets Cyber Threats to Electric Grid, Legis1’s report on the Quantum-GUARD Act introduced by Sens. Coons and Rounds.