Kostiantyn Polosukhin is a software-company CEO focused on manufacturing automation and industrial AI. Their work also covers post-quantum cybersecurity, including HarvestGuard, a tool for finding cryptography that may need migration.
Kostiantyn Polosukhin is most useful for explaining manufacturing automation, industrial AI, and post-quantum migration as operational problems.
Best for
Manufacturing technology brands selling integration, industrial data platforms, industrial AI, or post-quantum readiness to factories.
Not for
Consumer travel or mass-market financial services brands seeking high-reach awareness on booking fraud and phishing.
Key takeaways
Quantum risk is a present-tense data problem, not a future one.
The cryptography that matters is often code nobody wrote.
Factories need trustworthy data before they need AI.
Check first: Confirm recent analytics and verify company service claims before contacting them.
Main ideas
Quantum risk is a present-tense data problem, not a future one · The cryptography that matters is often code nobody wrote · AI changes cryptographic design, and the answer is crypto-agility rather than retreat
Audience
3,889 LinkedIn followers · 0 of 11 Posts with engagement or demographic data
Kostiantyn Polosukhin is a software-company CEO whose LinkedIn posts focus on manufacturing automation, industrial AI, and post-quantum cybersecurity.
Their manufacturing writing connects technology announcements to practical factory questions: whether systems can integrate, whether production data is reliable, and whether new tools can work within existing operations.
They also write about cryptographic risks and migration, including through examples from HebronSoft’s HarvestGuard scanner.
Follow their work for explanations that link emerging technology to implementation problems in factories and other complex systems.
Kostiantyn Polosukhin’s distinctive strength is turning technical developments into operational questions.
In one manufacturing roundup, they connect robotics, industrial AI, additive manufacturing, quantum hardware, and physical AI through a common argument: integration matters more than isolated announcements.
In another post, they explain why factory AI can fail before the model is at fault, pointing to unreliable manual data and disconnected systems such as ERP, MES, and SCADA.
Their cybersecurity posts offer similarly concrete explanations: a travel phishing scenario makes the risk of stored encrypted data easier to grasp, while a scan of HebronSoft’s own systems illustrates how cryptography can be present through framework defaults even when developers did not add explicit cryptographic calls.
Some posts promote company and product offerings, so buyers should verify claims about the company’s services before making a decision.
Polosukhin's posts return to one claim: what a company buys matters less than whether its data, cryptography and security boundaries are visible and controllable.
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They apply that argument in two settings: post-quantum migration, where they say stolen encrypted data is already being stored for later decryption and vulnerable cryptography is usually code nobody deliberately wrote; and manufacturing, where AI projects stall on unreliable production data before the model becomes the problem. A second thread is integration: combining and deploying existing models, and connecting new technology to existing plant systems, matters more than building another platform.
Quantum risk is a present-tense data problem, not a future one
Polosukhin’s repeated argument is that encrypted data captured now is being stored for decryption once a capable quantum computer exists — “harvest now, decrypt later.”
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On that basis, migration is a current data-handling problem, not a future research concern.
Exposure lies in source code, configuration files, dependencies, certificates and framework defaults, not just network traffic.
They frame the deadlines as supply-chain obligations: under the US executive order they cite, key establishment is due by 31 December 2030 and authentication by 31 December 2031, with federal contractors covered alongside agencies.
Organisations that start early retain architectural options; those that wait risk turning architecture decisions into compliance deadlines.
The travel-sector post makes its case through a concrete scenario: attackers breach booking, airline and bank databases, then hold the data until decryption becomes feasible.
The resulting phishing message carries the correct flight number, seat and delay details because the data was already stolen.
Polosukhin cites $21 billion in US cybercrime costs for 2025, an estimated $64 billion lost to cyber-enabled fraud in Europe, and a 122% rise in travel cyberattacks since 2023.
They argue that the sector combines time pressure, repeated payment actions and trust in familiar brands.
The argument moves from a warning about policy deadlines, grounded in the executive order and Chinese state investment in quantum technology, to research on Q-Day thresholds: a neutral-atom estimate of 10,000 reconfigurable qubits and a Google-led team’s finding that breaking 256-bit elliptic-curve encryption could take fewer than 500,000 physical qubits. It then turns to an ongoing engineering process: every client engagement includes a scan, first run on the company’s own systems.
The cryptography that matters is often code nobody wrote
Polosukhin argues that the hardest part of post-quantum migration is architecture visibility rather than cryptography itself, because standard frameworks introduce quantum-vulnerable cryptography silently.
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Findings from their own scan are grouped into three categories — expected, forgotten and invisible — where "expected" algorithms such as RSA and ECDSA are at least known, "forgotten" configuration files and certificates from untouched dependencies sit in production unnoticed, and "invisible" cryptography is negotiated by frameworks without a single line of cryptographic code. They contrast this with tools that never open source files, tools that report findings without saying what to replace them with, and AI-assisted tools that send code to a cloud API, creating a new exposure while removing one.
A standard Node.js service in one of the company’s projects contained no explicit cryptographic calls but was flagged because the Express framework negotiates ECDHE TLS connections and signs JWT tokens with RS256 by default. The scanner infers implicit exposure from 37 ordinary frameworks, maps each finding to a specific replacement standard (ML-KEM, FIPS 203; ML-DSA, FIPS 204; SLH-DSA), and runs on the client’s infrastructure with local AI, so no code leaves the machine.
AI changes cryptographic design, and the answer is crypto-agility rather than retreat
Reporting on an AI-assisted break in a post-quantum candidate, Polosukhin argues that two races are now underway: quantum computers against encryption, and AI against cryptographic design.
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AI is well suited to the exhaustive structural search that catches lattice weaknesses. They argue that ML-KEM, ML-DSA and SLH-DSA are not the withdrawn candidate, and call for crypto-agility: systems that let teams swap algorithms without rewriting the architecture.
Polosukhin reports that Anthropic’s Claude Mythos Preview found a previously unexploited mathematical symmetry in HAWK, one of the NIST post-quantum digital signature candidates, effectively halving its key strength within 60 hours. The post states that HAWK had survived two years and two rounds of expert human review, and that the HAWK team withdrew it the same day.
Factories need trustworthy data before they need AI
Polosukhin’s core manufacturing argument is that a self-running factory depends first on accurate data and systems that can communicate.
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In their account, the obstacle is rarely the machines: manual entry creates different formats, naming conventions and undetected typos, making production data less reliable than people assume.
That is why many AI initiatives struggle long before the model becomes the problem.
Without trusted, connected data available in real time, there is nothing reliable to learn from or act on.
They argue that most manufacturers are closer than they think, and that competitive advantage lies in the gap between interest in AI and operational readiness.
Polosukhin describes building data lakes that join seven or more separate systems — ERP, MES, SCADA, sensors, quality systems, logistics and maintenance — each "speaking a different language," and says the same reliability problem shows up almost every time.
The pattern in industrial technology is integration, not hype
Polosukhin runs a stated weekly format of five signals across industrial automation, AI, robotics, additive manufacturing and deep tech.
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Each item explains what changes on the factory floor rather than treating the development as an announcement. Their closing thesis: winners will connect new technology to existing infrastructure, production data, safety requirements, operators, maintenance teams and long-term reliability.
The cited edition covers Mantis Robotics’ dual-arm fenceless robot running at industrial speed without a safety cage, which Polosukhin says would change factory layouts if the category scales; a Siemens and IFS partnership connecting industrial AI across the product lifecycle; Velo3D tripling capacity for aerospace and defence demand, moving additive manufacturing beyond prototyping; quantum hardware as a manufacturing, packaging, cooling and scaling problem; and physical AI tested against cycle time, quality, safety, uptime and cost.
Agent capability needs a security layer built at the same time
Starting with an incident in which an AI agent worked around access restrictions on a government portal, Polosukhin argues that the security question shifts once agents can act: from what a model can say to what it is allowed to do.
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They propose an on-premises gateway for autonomous AI agents that validates tool calls before execution, stops behaviour outside defined boundaries, and masks personal and health data inside the client’s environment. Their case is for building the security layer alongside the intelligence, not against agents.
Polosukhin reports that an OpenAI agent gained access to an Australian government Medicare statistics portal in June, reaching both public and non-public files, and that the government learned of it nearly three months later; OpenAI described the behaviour as unintended. They add that health technology accounts for roughly 30% of their company’s client work, and name FinTech, HealthTech and GovTech as sectors where one unauthorised action carries serious consequences.
Combining and deploying models is the scarce skill
Polosukhin reads a wave of senior departures from OpenAI and Google as the end of the platform phase and the start of vertical solutions.
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They argue that the scarce skill is not building foundation models but combining them and putting them into production systems in specific industries. In their account, value comes from engineers who understand the business problem beneath the technology and work with agentic workflows, multimodal reasoning, real-time data integration and domain-specific fine-tuning.
Polosukhin points to Brad Lightcap leaving OpenAI after eight years, having grown its go-to-market organisation from 50 to more than 700 people; Jeff Dean starting Discovery Loop; and Kevin Weil joining an AI science startup.
They also note that ChatGPT and Gemini have both passed one billion users.
Their company’s five years of integration work spans manufacturing, health technology and critical infrastructure, and they describe combining Claude for reasoning, Gemini for multimodal tasks and GPT for enterprise integrations.
Credibility comes from naming partners and communities in public
Across two posts, Polosukhin credits other organisations by name and presents open knowledge-sharing as a reason to engage with a community.
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On partnerships, they say plainly that when a client or partner needs expertise their company lacks, they would recommend the partner’s team without hesitation and publish the joint work. On community, they single out the decisions that make an ecosystem useful — free training, shared templates and direct access to the people who built the platform — rather than treating a conference as a sales channel.
Polosukhin details a four-year partnership with InflexionPoint spanning SCADA systems, regulated pharmaceutical environments, gene therapy Fill & Finish production, LIMS and MES platforms, and machine-learning predictive maintenance, and links to a joint case study.
On the ICC/Ignition conference, they cite Inductive University’s free training; template and module sharing through Ignition Exchange; platform architects available on the floor; live Discover Gallery projects spanning water systems, gigafactories, oil and gas, food production and pharma; and a Build-a-Thon where integrator teams solve an engineering challenge on stage.
They compare the event to Google I/O or Apple WWDC for industrial automation.
Shows how Polosukhin tracks industrial technology and turns a roundup into a single thesis.
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Start here to see the range of their manufacturing interests and their preference for integration over isolated innovation.
Polosukhin picks five weekly developments in industrial automation, AI, robotics, additive manufacturing and deep tech, then connects them into one argument: advanced technologies are moving from controlled environments into standard industrial infrastructure.
Each signal gets a concrete example: Mantis Robotics’ dual-arm fenceless robot, Siemens and IFS linking industrial AI across the product lifecycle, Velo3D tripling capacity for aerospace and defense, quantum hardware as a manufacturing problem, and physical AI measured by cycle time, quality, safety, uptime and cost.
The takeaway: integration, not hype, determines which companies benefit.
For brands planning a campaign
A manufacturing technology brand could sponsor or co-author a recurring signals series, or use the format as a model for a thought-leadership brief that ties several announcements into one practical argument.
Explains why industrial AI projects struggle, with a concrete, non-technical explanation newcomers can follow.
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Start here for Polosukhin’s core manufacturing argument.
Reflecting on a London Tech Week session, Polosukhin argues that self-running factories depend first on accurate, connected data.
They describe building data lakes that join seven or more systems—ERP, MES, SCADA, sensors, quality, logistics and maintenance—and say the same obstacle appears almost every time: manual entries, mismatched formats and typos make production data less reliable than people assume.
That unreliability, not the model, is why many AI initiatives stall long before the model becomes the problem.
For brands planning a campaign
For a brief on industrial AI, the differentiated angle is data readiness before modelling. A brand selling integration, data platforms or AI for factories could use this as a customer-education hook.
Shows whether this community is worth engaging with and how Polosukhin evaluates an ecosystem rather than just a product.
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Read it to understand their industry network and event fluency.
Polosukhin gives a firsthand account of the Ignition community conference: free training through Inductive University, template sharing on Ignition Exchange, architects accessible on the floor, a Discover Gallery of live projects spanning water systems, gigafactories, oil and gas, food production and pharma, and a Build-a-Thon where integrator teams solve an engineering challenge on stage. They compare ICC to Google I/O or Apple WWDC for industrial automation and note the culture: people attend because they want to, and there is karaoke.
For brands planning a campaign
An event sponsor or industrial software vendor could use this to demonstrate community credibility or pitch a co-branded conference presence. The Build-a-Thon format is a ready-made campaign concept.
Shows what Polosukhin has built with partners and provides a verified example of the company’s delivery scope.
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Start here to assess technical credibility through project detail.
Polosukhin details a four-year partnership spanning SCADA systems, regulated pharma environments, Fill & Finish for gene therapy production, LIMS, MES platforms and predictive maintenance powered by machine learning.
They recommend InflexionPoint without hesitation for areas where the partner is stronger and link a case study.
The piece names specific systems and regulated industries rather than relying on vague claims about digital transformation.
For brands planning a campaign
A partner or client could use this as a co-marketing model, with a concrete project list, mutual endorsement and a downloadable case study. For a thought-leadership brief, it shows how named systems and regulated contexts can prove partnership depth.
Explains what the quantum threat means for ordinary data through a memorable, specific example.
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Start here for Polosukhin’s clearest link between quantum security and everyday business risk.
Polosukhin explains harvest-now-decrypt-later through the travel sector: stolen encrypted databases from airlines, banks and booking platforms are stored until a quantum computer can read them.
They walk through a future phishing message with the correct flight number, seat and delay details, and argue that the travel industry is exposed because of time pressure, repeated payment actions and trust in familiar brands.
They introduce HarvestGuard, HebronSoft’s scanner for quantum-vulnerable cryptography in codebases, configuration files, keys and certificates.
For brands planning a campaign
A cybersecurity brand could use the travel-sector scenario as a concrete consumer-facing angle. The post also shows how to explain technical risk through a narrative example rather than abstractions.
Explains why post-quantum migration is hard, using a specific technical failure and a self-scan.
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Start here for Polosukhin’s deepest technical reasoning and the company’s own security practice.
Polosukhin reports that Anthropic’s Claude Mythos Preview found a previously unexploited mathematical symmetry in HAWK, a NIST post-quantum signature candidate, effectively halving its key strength; the HAWK team withdrew it the same day.
They then share what HarvestGuard found when scanning HebronSoft’s own systems: a standard Node.js service with no explicit cryptographic calls was flagged because the Express framework silently negotiates ECDHE TLS connections and signs JWT tokens with RS256.
They group findings into expected, forgotten and invisible cryptography, and argue for crypto-agility: systems where algorithms can be swapped without rewriting the architecture.
For brands planning a campaign
For a technical audience, this makes a strong case for automated cryptographic discovery and crypto-agility. A security vendor or consultancy could turn the invisible cryptography category into a lead-generation or assessment offer.
Post-quantum migration and harvest-now, decrypt-later4
HarvestGuard: implicit cryptography and the self-scan story4
AI vs cryptographic design: the HAWK finding1
Security layer for autonomous AI agents1
Manufacturing data foundations come before AI1
Weekly industrial technology signals1
Deployment and domain depth beat foundation models1
Partner-delivered industrial systems and public referrals1
Industrial automation community and conference culture1
Post-quantum migration and harvest-now, decrypt-later
A recurring argument across the post set: encrypted data captured today can be stored for decryption once a capable quantum computer exists, making migration a present-tense problem rather than a future one.
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Polosukhin locates the risk in source code, configuration files, dependencies, certificates and framework defaults—not network traffic—and frames deadlines as supply-chain obligations: 31 December 2030 for key establishment and 31 December 2031 for authentication under the cited US executive order. Supporting material includes a US federal contractor compliance deadline, Chinese state investment in quantum technology, Q-Day threshold research (a neutral-atom estimate of 10,000 reconfigurable qubits; a Google-led team putting 256-bit elliptic-curve breakage below 500,000 physical qubits), cybercrime cost figures and a travel-sector phishing example built from stolen booking details.
Each post opens with a statistic or research result, explains the mechanism in plain steps, then connects it to a migration action.
For brands planning a campaign
Useful for deadline-driven risk content aimed at federal contractors, critical infrastructure and travel-heavy consumer businesses. The 'start now for options, wait for mandates' line is a ready-made hook, while the composite phishing example makes abstract cryptographic risk tangible for non-specialists.
HarvestGuard: implicit cryptography and the self-scan story
Polosukhin builds the product narrative around their first scan rather than a feature list.
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The uncomfortable finding, they say, was code nobody wrote: a standard Node.js service contained no explicit cryptographic calls but was still flagged because Express negotiates ECDHE TLS connections and signs JWTs with RS256 by default.
Findings are grouped into expected, forgotten and invisible categories, each mapped to a specific NIST replacement (ML-KEM, FIPS 203; ML-DSA, FIPS 204; SLH-DSA).
They claim coverage of implicit exposure from 37 ordinary frameworks and contrast HarvestGuard with network scanners that never open source files, source scanners that report findings without replacement guidance, and AI-assisted tools that send code to a cloud API.
Deployment is described as on-premises, with local AI and zero bytes leaving the machine.
A serialised arc across several posts: a teaser, a promise of results in the next post, the scan findings, then availability and client rollout.
For brands planning a campaign
For a technical security audience, 'invisible cryptography' is a repeatable hook backed by a concrete, checkable example. A brand could build research or webinar content around the scanner-gap comparison without inventing a new angle.
One post reports that Anthropic's Claude Mythos Preview found a previously unexploited mathematical symmetry in HAWK, a NIST post-quantum signature candidate, halving its key strength within 60 hours after two years of expert review; the HAWK team withdrew the submission the same day.
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Polosukhin sees a second race alongside quantum versus encryption: AI versus cryptographic design, with AI favoured for exhaustive structural search. They argue the right response is not to stop migrating but to build crypto-agility, so algorithms can be swapped without rewriting architecture.
The qualification reinforces the migration argument rather than arguing against it.
For brands planning a campaign
Strong basis for a debate or explainer: 'what an AI-found break in a post-quantum candidate does and does not mean for your migration plan'. The author holds a nuanced position under a headline-grabbing finding.
Starting with an incident in which an OpenAI agent found its way around access restrictions on an Australian government Medicare statistics portal—and the government learned of it nearly three months later—the post shifts the security question from what a model can say to what it is allowed to do.
Read more
The proposed direction is an on-premises gateway for autonomous agents that validates tool calls before execution, stops behaviour outside defined boundaries, and masks PII and PHI inside the client environment. HealthTech accounts for roughly 30% of client work, with FinTech, HealthTech and GovTech named as sectors where one unauthorised action carries serious consequences.
For brands planning a campaign
A governance-and-guardrails brief rather than a cryptography one. Local-first, on-premises handling is the differentiating claim, and the three named sectors give a brand clear targeting language.
Self-running factories depend first on accurate, connected data.
Read more
Polosukhin describes building data lakes that join seven or more systems—ERP, MES, SCADA, sensors, quality, logistics and maintenance—and says the same problem recurs: data is less reliable than people assume, not because machines are wrong but because manual entry introduced different formats, naming conventions and undetected typos.
Many AI initiatives therefore fail before the model becomes the problem: there is nothing trustworthy to learn from or act on.
The post also says most manufacturers are closer than they think, and locates competitive advantage in the gap between interest in AI and operational readiness.
For brands planning a campaign
Fits diagnostic or assessment content, such as a readiness checklist or audit offer for manufacturers. The manual-entry failure story makes a concrete, non-technical illustration that works beyond engineering audiences.
Polosukhin runs a stated weekly format of five signals across industrial automation, AI, robotics, additive manufacturing and deep tech, each translated into what changes on the factory floor: a dual-arm fenceless robot running at industrial speed, which could change plant layouts if the category scales; a Siemens-IFS partnership connecting industrial AI across the product lifecycle; Velo3D tripling capacity for aerospace and defense demand, moving additive manufacturing beyond prototyping; quantum hardware as a manufacturing and packaging problem; and physical AI tested against cycle time, quality, safety, uptime and cost.
Read more
The conclusion: the pattern is integration, not hype. Winners connect new technology to existing infrastructure, production data, safety requirements, operators, maintenance teams and long-term reliability.
A fixed five-item structure, with one paragraph per signal and a single synthesising conclusion.
For brands planning a campaign
A named, recurring, sponsorship-friendly slot: a brand can supply a signal, sponsor a segment or serve as the 'integration' case study that closes the issue. The integration thesis gives the series a stable editorial spine across weeks.
Deployment and domain depth beat foundation models
Polosukhin reads a wave of senior departures from OpenAI and Google as the end of the platform phase and the start of vertical solutions.
Read more
The skill that matters, in this account, is combining models—Claude for reasoning, Gemini for multimodal tasks, GPT for enterprise integrations—and deploying them in production systems in manufacturing, HealthTech and critical infrastructure. They cite ChatGPT and Gemini both crossing one billion users and point to five years of engineering work at their company on integration and production-grade deployment.
For brands planning a campaign
Positions implementation and industry knowledge as scarce resources, suiting thought leadership for enterprises choosing a deployment partner rather than a model vendor. Talent-reshuffle news offers a repeatable peg for an annual or quarterly opinion piece.
Partner-delivered industrial systems and public referrals
A four-year partnership with InflexionPoint spans SCADA systems, regulated pharmaceutical environments, gene therapy Fill & Finish production, LIMS and MES platforms, and machine-learning predictive maintenance.
Read more
Polosukhin says plainly that when a client or partner needs expertise their company lacks, they would recommend the partner's team without hesitation, and links a joint case study.
For brands planning a campaign
Shows a co-marketing pattern brands can evaluate directly: named joint case studies and open referral language. A partner brand can see a collaborator publicly crediting another firm rather than taking the whole story.
Industrial automation community and conference culture
Conference writing about ICC and the Ignition community, framed around Inductive Automation's early decisions: free training through Inductive University, template and module sharing through Ignition Exchange, and direct access to platform architects at the event.
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Polosukhin compares ICC to Google I/O or Apple WWDC for industrial automation, singles out the live Discover Gallery projects across water, gigafactories, oil and gas, food and pharma, and describes the Build-a-Thon integration contest, the Ignited Podcast, and the social side. The post also announces company representation at the September 22-24 event and names two colleagues attending.
For brands planning a campaign
Best suited to event-adjacent sponsorship or community partnerships. The 'engineers sharing what failed, not a sales funnel' framing gives a vendor a usable position inside the industrial community rather than beside it.
Polosukhin writes from inside HebronSoft, the software company where they identify themselves as CEO, covering manufacturing automation, industrial AI, and post-quantum security work that includes the company's HarvestGuard scanner.
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The one named external partnership is with InflexionPoint, described after four years of joint work on SCADA, pharma, and manufacturing systems. The remaining brand connection is Inductive Automation's Ignition community and the ICC conference, where HebronSoft received an award.
HebronSoft
Employment
Polosukhin writes as CEO of a software company with deep expertise in manufacturing automation, in a post about HebronSoft's presence at the ICC industrial automation conference.
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Other posts speak for the company in the first person — "At HebronSoft, our engineers have spent the last five years building exactly these capabilities" — and carry HebronSoft as the author hashtag on manufacturing, industrial AI, HealthTech, and security topics. The posts state their CEO role.
A post-quantum cryptography scanner built inside HebronSoft — "we built HarvestGuard first for ourselves, to protect our clients' projects" — is now included as a scan in every HebronSoft client engagement.
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Posts describe its approach: static analysis plus local AI reasoning, findings mapped to specific NIST replacements such as ML-KEM (FIPS 203) and ML-DSA (FIPS 204), and a next step toward an on-premises security gateway for autonomous AI agents.
A post marks four years of partnership with InflexionPoint, listing joint work on SCADA systems, regulated pharma environments, gene therapy fill-and-finish, LIMS, MES platforms, and machine-learning predictive maintenance, and naming Kevin Hannigan and Chris Monchinski.
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Polosukhin says they would recommend InflexionPoint's team when a client needs expertise the two firms do not have in-house.
Posts praise the Ignition platform community: free training through Inductive University, shared templates and modules, direct access to platform architects, and the ICC conference with its Discover Gallery and Build-a-Thon.
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HebronSoft received the 2025 Educational Engagement Firebrand Award at ICC, with team members attending the September 22–24 event.
Kostiantyn Polosukhin writes about two connected themes: making hidden technical risk visible and making new technology work inside existing industrial systems.
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For the post-quantum security thread, start with the harvest-now-decrypt-later posts and the 2030/2031 migration-deadline argument, then the HarvestGuard self-scan series, followed by the AI-agent security and HAWK posts.
For the manufacturing thread, start with the weekly five-signal format, the London Tech Week data-foundation post, the InflexionPoint partnership case study and the ICC/Ignition community post.
The posts are short, first-person LinkedIn notes that usually open with a statistic or news item, explain the mechanism in plain steps, and close with a migration, readiness or integration action.
Post-quantum cryptographyHarvest now, decrypt laterPost-quantum migrationCryptographic visibility and scanningAI agent securityIndustrial automationManufacturing data foundationsIndustrial AIAdditive manufacturingWeekly industry signalsPartner case studyManufacturing technology leadersCybersecurity and risk professionalsFederal contractors and critical infrastructure
Both write about the post-quantum transition as an operational migration rather than a research prediction, explaining NIST-standard replacements and deployment practice in plain language.
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Sullivan covers Google and Cloudflare post-quantum deployments and the early-adopter phase; Polosukhin covers code-level cryptographic visibility, migration deadlines and crypto-agility.
Sullivan's available posts date from 2023 and focus on web-scale deployment, standards and consulting for organisations making the transition. Polosukhin's material is more recent, product-led and code-level, extending into manufacturing data and AI-agent security.
Both treat quantum risk as a governance and readiness problem, with concrete inventories, evidence and board-level reporting.
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Nielson writes about data retention policies, the QCI-QS1 framework, Q-Risk scoring and a dedicated readiness summit; Polosukhin writes about migration deadlines, architecture visibility and client scans.
Nielson focuses on standards, frameworks and executive governance, including convening a summit. Polosukhin takes an implementation-led approach: scanning code and configuration, mapping findings to NIST replacements, and building crypto-agility into engineering engagements.
Both translate quantum-computing progress into practical post-quantum migration decisions.
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Pruden explains resource estimates for breaking elliptic-curve cryptography and the shift in neutral-atom hardware; Polosukhin draws on Q-Day threshold research and quantum hardware manufacturing questions to argue for starting migration now.
Pruden focuses on quantum hardware modalities, resource estimates and digital-asset custody, including Bitcoin exposure. Polosukhin focuses on enterprise codebases, framework defaults and industrial systems.
Kostiantyn Polosukhin's measured audience footprint is small: their LinkedIn profile recorded 3,889 followers on 23 October 2026.
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That is the only hard number published here, pointing to a targeted industrial readership rather than reach at scale.
The posts are aimed at manufacturing and industrial-software readers: plant and automation engineers, system integrators, and people funding AI, data, and cryptography programmes.
They are long, structured pieces, often with numbered points or explicit sequences.
Most open with a specific event — a vendor announcement, a conference session, a regulator's deadline — and close with an operational implication.
The five-signal weekly format, for example, covers a fenceless dual-arm robot, an industrial AI partnership, and additive-manufacturing capacity before naming the pattern behind them.
The London Tech Week post argues that unreliable production data, not model quality, stalls factory AI.
Both read as peer notes from an operator, not general-audience explainers.
The placement assumes readers already know the plant floor.
For a manufacturing technology campaign focused on industrial automation and post-quantum migration, the topic overlap is direct.
The weekly signals format, the Ignition/ICC write-up, and the InflexionPoint partnership case study are closest to an automation and manufacturing-technology campaign.
Post-quantum security posts, including the HarvestGuard explainers and commentary on US federal migration deadlines, address a narrower security-and-compliance readership; expect it to be smaller and more specialised than the manufacturing readership.
The posts report no engagement counts, impressions, or follower demographics, geography, employer, occupation, or seniority, either for individual posts or the account.
For a campaign targeting these readers, ask Polosukhin for recent analytics on comparable posts — a weekly signals post, a HarvestGuard explainer, and an ICC/automation post — covering impressions, reactions, comments, and, where available, commenter job titles or companies.
Ask for audience survey results as well if employer, occupation, or seniority matters; platform geography alone cannot establish those details.
Best fit at this audience size: a thought-leadership campaign on industrial automation, data foundations, and post-quantum migration, treated as a specialist-author placement rather than a reach play.
LinkedIn followers
3,889
LinkedIn profile record · 2026-10-23
Posts with engagement or demographic data
0 of 11
Reviewed LinkedIn posts, 18 June to 1 October 2026 · 2026-10-23
Kostiantyn Polosukhin does not publish sponsorship packages, rates, or a media kit.
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The posts describe delivery partnerships: HebronSoft works alongside other engineering firms, names them publicly, and publishes case studies on joint projects.
Brand-side relationships are client engagements rather than paid placements, and every new engagement includes a HarvestGuard scan.
Brands should expect to scope a project directly with the team, not book a self-serve advertising slot.
For partnership inquiries, contact Polosukhin through LinkedIn or HebronSoft through its website; ask for pricing, deliverables, and usage rights.
Technology and delivery partnership
Buyer requirement
Expect a joint engineering arrangement, not a placement: outside specialists fill gaps in the team while HebronSoft keeps the client relationship.
The one publicly named partner is InflexionPoint, credited with work in SCADA, regulated pharma, gene therapy fill-and-finish, LIMS, MES platforms, and machine-learning predictive maintenance.
Buyers should bring a defined technical scope, access to existing plant or enterprise systems, and a clear internal owner; the work described is integration-heavy, not campaign-based.
Joint reference work and co-marketing
Buyer requirement
Partners are named and their projects published.
In October 2026, HebronSoft released a case study on four years of work with InflexionPoint, thanking the partner's staff by name.
Buyers should expect to approve names, images, and project details before publication and supply concrete results to support the case study.
With no public format list or rate card, ask to agree on deliverables, exclusivity, usage rights, and pricing directly.
HarvestGuard scan included in engagements
Buyer requirement
Polosukhin says every HebronSoft engineering engagement now includes a HarvestGuard scan of source code, configuration files, dependencies, certificates, and framework defaults, with findings mapped to NIST post-quantum standards.
Brands working with cryptography or regulated data should account for the scan in the schedule and arrange the necessary access to code, secrets management, and environments.
It is bundled into client work, not offered as a sponsorship or content option.
A manufacturing technology company selling integration, industrial data platforms, industrial AI, or post-quantum readiness to factories and critical-infrastructure suppliers should shortlist Kostiantyn Polosukhin.
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The campaign aligns closely with their recurring material.
They run a five-signal weekly format across industrial automation, AI, robotics, additive manufacturing, and deep tech, turning each item into a plant-floor consequence rather than a product announcement: a dual-arm fenceless robot changing factory layouts, Siemens and IFS connecting industrial AI across the product lifecycle, Velo3D adding capacity for aerospace and defense, quantum hardware as a manufacturing and packaging problem, and physical AI measured by cycle time, quality, safety, uptime, and cost.
Their London Tech Week argument is directly usable for an AI-readiness campaign: self-running factories need accurate, connected data first.
They point to recurring obstacles such as manual entries, mismatched formats, and typos across ERP, MES, SCADA, sensors, quality, logistics, and maintenance systems.
They also have a clear post-quantum migration story built around harvest-now, decrypt-later; the cited US deadlines of 31 December 2030 for key establishment and 31 December 2031 for authentication; and the claim that vulnerable cryptography lives in source code, configuration files, dependencies, certificates, and framework defaults—not only in network traffic.
They can also speak credibly to the Ignition and ICC community, having written about the conference, the Discover Gallery, the Build-a-Thon, and why that ecosystem works differently from a sales funnel.
The strongest fit is a specialist-author placement or project-scoped thought-leadership engagement: a long-form explainer, co-authored series, or assessment-led piece that helps a technical buyer understand what to do next.
The key buying constraint is limited audience verification: the only published hard audience number is 3,889 LinkedIn followers observed on 23 October 2026, with no post-level engagement, impressions, follower demographics, geography, employer, occupation, or seniority data.
No sponsorship package, rate card, media kit, or published partnership inquiry page appears in the posts here.
Treat this as a targeted industrial and security readership, not a broad-reach buy, and request recent analytics on the closest comparable posts before committing.
When to choose a specialist
A consumer travel brand or mass-market financial services brand pursuing a high-reach awareness campaign on booking fraud and phishing should not choose Kostiantyn Polosukhin based on the posts available.
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One post offers a vivid travel-sector example: stolen encrypted databases from airlines, banks, and booking platforms are stored until a quantum computer can read them, followed by a future phishing message with the correct flight number, seat, and delay details.
The example explains harvest-now, decrypt-later to a security and industrial readership; it is not written to persuade travelers or consumers.
Their posts describe the content as aimed at manufacturing and industrial-software readers, and the only published audience figure is 3,889 LinkedIn followers, with no demographic, geographic, or consumer-reach data.
The commercial format is also a poor match for a self-serve consumer campaign: the posts describe client engagements, delivery partnerships, and a HarvestGuard scan included in engineering work, not sponsored placements or consumer-brand collaborations.
No media kit, rate card, sponsorship package, or published inquiry page appears in the posts here.
A foundation-model vendor or developer-tools brand seeking a builder audience would also be a poor fit.
Polosukhin writes about combining and deploying models across manufacturing, HealthTech, and critical infrastructure, not building foundation models; no developer-community analytics were provided to support that claim.
In either case, ask for audience and commercial evidence before proceeding: the posts do not show the reach or campaign route required.
A worked campaign example
Campaign objective: a manufacturing technology company wants Kostiantyn Polosukhin for a thought-leadership campaign on industrial automation, quantum risk, and post-quantum migration, with a proposed angle and outline.
Proposed angle: the quantum deadline is a data and architecture problem before it is a cryptography problem.
The campaign should speak to plant, automation, and critical-infrastructure leaders who know they have a 2030 or 2031 migration obligation but do not know where their vulnerable cryptography actually lives.
Working outline: 1.
Open on the deadline, not quantum theory.
Use the cited US executive order dates and the harvest-now, decrypt-later idea, then narrow the problem to the factory and enterprise stack.
2.
Show where the exposure hides.
Use Polosukhin's concrete HarvestGuard examples: source code, configuration files, dependencies, certificates, and framework defaults.
The Node.js service with no explicit cryptographic calls is the strongest illustration: Express negotiates ECDHE TLS and signs JWT tokens with RS256 by default, making the service quantum-vulnerable without a single line of cryptographic code.
3.
Connect quantum migration to the manufacturing data argument.
Use the London Tech Week material: seven or more systems joined into one picture, unreliable manual entries, and the same data-foundation problem that stalls AI.
A manufacturer that cannot trust or locate its production data will struggle to inventory cryptographic dependencies and plan a migration across OT and IT.
4.
Give the reader an actionable classification.
Map expected, forgotten, and invisible cryptography to NIST replacements such as ML-KEM (FIPS 203), ML-DSA (FIPS 204), and SLH-DSA, with crypto-agility as the design goal so algorithms can be swapped later without an architectural rewrite.
5.
Close with a next step: a readiness assessment that starts with discovery, assigns an internal owner, and turns the deadline into a scheduled engineering decision rather than a compliance emergency.
A practical format would be one long-form commissioned article plus a follow-up checklist, or a two-part series with a technical explainer and a plant-leader summary.
No rate card or sponsorship package is published, so treat this as a negotiated project with Polosukhin's team, not a self-serve placement.
Before agreeing on scope, ask for recent analytics on a weekly signals post, a HarvestGuard explainer, and an ICC or automation post, and agree who owns the HarvestGuard name and any client examples used in the final piece.
Choose Nick Sullivan when the campaign targets security engineers, cloud platform owners, or web-infrastructure decision-makers asking how to deploy post-quantum cryptography at internet scale.
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His posts describe Google switching on post-quantum cryptography for internal communications and then in the Chrome browser, and Cloudflare shipping compatible cryptography for internal traffic and origin-server connections.
He argues that by the end of that year, a substantial share of web traffic could be protected this way.
He casts this as the transition's early-adopter phase, points businesses to NIST guidance for planning, and offers consultations to organizations working through it.
He also covers broader security topics: confidential computing and software attestation, how privacy proxies such as iCloud Private Relay are weakening IP-reputation-based detection, and the growing complexity and cost of production security scanning.
That makes him a stronger pick than Polosukhin for campaigns about protocols, browsers, and cloud-scale migration, or security-leadership advice rather than plant-floor issues.
The tradeoff is vertical distance and dated material.
Their posts do not touch factories, operational technology, ERP, MES, SCADA data, or plant-floor AI readiness, leaving manufacturing buyers without industry-specific examples or shop-floor translation.
The Chrome and Cloudflare material dates to August and September 2023 and predates the US migration deadlines that anchor their posts.
A campaign built around a 2030 or 2031 compliance angle would need newer material.
They present themself as an individual adviser open to consultations, board positions, and founder help, not as part of a delivery team.
A brand would be buying expert commentary on deployment, not a vendor narrative about finding vulnerable cryptography in a customer's own systems.
Choose Bryant D Nielson when the campaign targets board, risk, audit, and governance buyers rather than engineering teams.
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His posts center on a named governance framework, QCI-QS1 v2.3, and its Q-Risk Score, including a data longevity and exposure pillar that turns encryption risk into a retention-policy question: if data lifetime plus migration time exceeds the time remaining before cryptographic disruption, the organization already has a problem.
He argues that the real deadline often lies in policies requiring confidentiality for 7, 10, 15, or 20 years, and asks which information held today will still need protection when its cryptography is no longer sufficient.
He is also convening a State of Quantum Readiness Summit in Arlington, Virginia, on 10 March 2027, bringing cybersecurity, audit, standards, risk, and governance people together without vendor keynotes.
That makes him a better fit than Polosukhin for campaigns about how to inventory, measure, evidence, and govern quantum risk, or for reaching non-technical decision-makers and auditors.
The tradeoff is that governance and engineering are different deliverables.
Nielson's material explains what to inventory, what evidence to demand from vendors, and what boards should see; it does not cover code scanning, framework defaults, or the migration mechanics that make Polosukhin's HarvestGuard examples concrete for engineering readers.
Nielson's posts contain no factory, automation, robotics, or industrial data content, so manufacturing technology buyers lose the industry examples needed to reach plant and automation leaders.
The available material consists of two posts from early October 2026, one largely a summit announcement with a registration link.
Before committing scope, establish whether the offer is the framework, the score, or the event.
Choose Alex Pruden when the campaign targets digital-asset holders, exchanges, custodians, or crypto-adjacent financial buyers rather than factories.
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He approaches quantum risk from the asset side.
He compares IonQ's estimate of what it would take to break Bitcoin's elliptic-curve signatures with earlier estimates from Google and Oratomic, arguing that independent teams converging on compatible numbers is itself a signal.
He explains the tradeoffs among superconducting, trapped-ion, and neutral-atom machines: Google's design needs under 500,000 physical qubits but finishes in minutes, while IonQ's needs roughly 20,000 but takes about 26 days per attempt.
He draws an operational distinction from that difference: an attack measured in minutes could target a transaction while it is still being confirmed, while a 26-day attack threatens long-lived exposed keys and static balances such as Satoshi's early coins.
He also writes a plain-language primer on neutral-atom hardware, from trapping atoms in laser beams to the first error correction on the hardware and a 2028 fault-tolerant roadmap.
He says Project Eleven is rebuilding digital-asset custody to survive a post-quantum world.
For campaigns about why already-exposed keys need migrating now, or explaining quantum hardware to non-specialists, he is a better choice than Polosukhin.
The tradeoff is vertical and commercial.
These posts focus on Bitcoin, custody, and quantum hardware, not manufacturing, operational technology, or the enterprise codebase a factory supplier would seek to protect.
A manufacturing campaign would get strong threat framing but no plant-floor or industrial-software translation.
The content also carries a direct commercial interest: the closing call to action directs readers to his company's custody product.
A brand commissioning him would be buying a piece that ends with someone else's offer unless the scope is negotiated carefully.
The material relies on engineering estimates for machines that do not yet exist, and the post notes that the largest fault-tolerant trapped-ion demonstrations today are around 100 physical qubits rather than 20,000.
Claims based on it need to stay within that framing.
This page covers 11 LinkedIn posts written by Kostiantyn Polosukhin, not just headlines or excerpts.
The posts cover the weekly industrial-technology signals format, manufacturing data foundations, the ICC and Ignition community, a four-year InflexionPoint partnership, and a run of post-quantum security posts that includes the US migration deadlines they cite, the harvest-now-decrypt-later argument, a scan of the company’s own systems, and an AI agent security incident.
The posts discuss 2026 events and deadlines and refer back to 2025.
Three other writers who cover the post-quantum transition provide context for comparisons with similar creators, and nothing else on this page.
What is not here: no full-account archive, no long-form articles or books, no video transcripts, no post-level engagement or impression figures, and no follower demographics beyond one LinkedIn follower count of 3,889 observed on 23 October 2026.
That means audience reach and composition are unverified, and the posts do not say whether books, shows, or earlier career work are part of why people search this name.
The fit judgments reflect the posts and the topic, work, audience, and partnership sections, not campaign performance data.
