Ajay G. writes about cybersecurity for small and mid-sized businesses, especially endpoint security, compliance, and protecting company data when AI agents use business tools. Ajay's posts also describe building security products at Kitecyber.
Most useful for SMB endpoint and AI-agent data security thought leadership and practical frameworks.
Best for
A security vendor selling endpoint and AI-agent data protection to SMBs for thought leadership or a webinar.
Not for
Fortune 500 identity-governance vendors targeting large-bank CISOs with video, newsletter takeovers, or guaranteed reach.
Key takeaways
SMB security needs fundamentals and integration, not a bigger stack.
AI agents decide faster than security can respond.
No single control sees the whole action.
Check first: Confirm LinkedIn follower analytics, recent post impressions and engagement, and any webinar attendance data.
Main ideas
SMBs need security fundamentals and integration, not a bigger stack · AI agents decide faster than security can respond · No single control sees the whole action
Ajay G. writes about cybersecurity for small and midsize businesses, focusing on endpoint protection, compliance, and keeping company data safe as AI agents use business tools.
Their work connects two practical concerns: smaller teams need manageable security, and traditional controls may miss what happens when people or AI agents move data between devices, applications, and services.
They also write about building Kitecyber, a security platform, combining industry commentary with a product perspective.
Ajay makes security problems easier to evaluate by turning broad concerns into questions and concrete actions.
A post on SMB security starts with a customer’s question about the minimum tools needed to feel secure and meet compliance needs, then lays out ten fundamentals, from multi-factor authentication and tested backups to monitoring and ongoing improvement.
In posts about AI agents, they shift attention from whether data moved to who or what moved it, why, and whether the action should have been allowed.
The “context gap” example explains why: endpoint, data-loss-prevention, and web tools may each see only one part of an agent’s activity.
This mix of practical checklists and specific explanations of where security visibility breaks down gives readers a useful way to think about everyday SMB priorities and newer AI-related risks.
Ajay’s posts also promote Kitecyber and its products.
Ajay's posts circle one argument from several angles: security got too complicated for the companies that most need it, and AI agents are making that worse.
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Ajay writes for small and mid-sized businesses, and the recurring priorities are cutting tool sprawl down to fundamentals, enforcing controls at the endpoint where data actually moves, and treating agent activity as a new kind of actor that existing data loss prevention was never designed to see. A secondary thread argues agents could let small companies compete with larger ones, paired with governance questions about what those agents may access and do.
SMBs need security fundamentals and integration, not a bigger stack
Ajay's most repeated position is that cybersecurity has earned a reputation for complexity, and small and mid-sized businesses absorb the cost.
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The question customers ask — what is the minimal set of tools needed to feel secure and meet compliance needs — should be simple to answer, but Ajay argues it has been made hard.
Most SMBs, in this view, do not need dozens of tools or deep security expertise; they need clarity, consistency, and coverage.
The trap is that each fundamental gets solved with a separate point product for endpoint, identity, compliance, and monitoring, recreating the complexity it was meant to remove.
Ajay describes the same pressure from the staffing side: modern SaaS, GenAI tools, coding agents, browser extensions, and cloud services running on SMB-grade staffing produce too many tools, too many tickets, too little time, and too much risk, while compliance expectations move downmarket and qualified security talent stays scarce.
A post opens with the customer question about the minimal tool set and answers it with ten steps: know what devices, users, and applications you have; enforce multi-factor authentication everywhere; patch operating systems and applications; protect endpoints; back up critical data and test the backups; limit access to what each person needs; monitor for unusual activity; train people continuously rather than once a year; document lightweight policies; and keep assessing and improving. Ajay argues these fundamentals are usually covered by disconnected point solutions, and points to a unified approach as the alternative.
Ajay argues that most data loss prevention was designed for humans and that agents invalidate its assumptions.
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Agents open files, read spreadsheets, call tools, and move content under a user's own login at machine speed.
To existing tools, that activity can look completely normal because it uses legitimate permissions.
The operative question shifts from whether sensitive data moved to who or what moved it, why, and whether that action should have been allowed.
Ajay sets out what a control has to do to keep up: classify data the moment an agent touches it on the device; distinguish whether a person acted or an agent acted as that person; allow, warn, or block before anything leaves; and keep tracking the data after it is converted, encoded, or screenshotted.
The framing is a race between agent speed and security speed.
One post opens with an agent making 400 decisions about company data with zero approvals, then contrasts that pace with a model, TypeSafe's Jev, built to return a typed decision with a probability in well under a second. Ajay's point is not the model itself but the mismatch: by the time a traditional alert fires, the agent has already made its next 400 decisions.
Ajay's recurring diagnosis of the fragmented stack is that each tool observes only a slice.
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Endpoint detection sees the process, data loss prevention sees the data, and the secure web gateway sees the connection, but nobody sees the whole action when an agent moves through several systems in one sequence.
Ajay proposes correlating user, device, application, data, process, and network activity where the action happens, rather than adding yet another specialized AI security tool.
The same idea appears in compressed form in a product launch post as a three-word loop: see, decide, enforce, continuously — with every attempt to read, copy, transform, upload, or share sensitive data by a human, a copilot, or an autonomous agent evaluated in real time and enforced on the spot.
A post walks through an AI agent that can access files, use SaaS applications, call APIs, browse the web, and act on a user's behalf in a single sequence, then asks which existing control sees the complete action. Ajay links the argument to a Slashdot article on the context gap and points readers to the CISO-level question: correlation, not tool count.
Passing an audit is not the same as protecting data
Ajay draws a line between compliance scope and what actually happens to data.
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Controls can be tested, audited, and passed while data still leaves through a route the audit never examined — the endpoint, the browser, or a generative AI prompt.
Ajay argues that the proof itself should change: which controls get revisited and what that proof should look like.
The theme also appears in lightweight documentation and continuous assessment as compliance fundamentals, and in automatic generation of audit-ready proof as a product capability.
One post states the argument flatly: a company can pass a SOC 2 Type II audit and still leak data every single day because the endpoint, browser, and GenAI prompt where the data actually left were never in scope. It promises to walk through which controls to revisit and how evidence should change.
Enforcement belongs at the endpoint, delivered through one agent
Ajay's proposed architecture follows from the context-gap and agent-speed arguments: the decision should sit where data actually moves, at the endpoint, not in gateways, prompt filters, or network appliances that see only part of a sequence.
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The delivery model is a single lightweight endpoint agent carrying data loss prevention, endpoint management, secure web and SaaS access, and zero-trust private access, with no gateways, appliances, or new infrastructure to deploy. The unified endpoint management material lists the same approach in operational detail: automatic enforcement of SOC 2, ISO 27001, and HIPAA policies; one agent handling compliance, device security, onboarding and offboarding, and remote access; cloud-native operation independent of Active Directory or legacy network dependencies; pre-built compliance templates; and support for Mac, Windows, and Linux with disk encryption, screen lock, admin privilege, SaaS monitoring, and GenAI usage controls.
A launch post cites a Stanford 2026 AI Index figure of 88% organizational AI adoption, a Microsoft finding that 46% of leaders say their organizations already use agents to automate workflows fully, and an IBM 2025 figure that 97% of organizations reporting an AI-related security incident lacked proper AI access controls — adoption nearly universal, controls nearly absent. It states that the new capability ships through the same lightweight agent as the existing products.
These two posts are the most commercially framed in the corpus: launch and product-capability announcements, while the fundamentals and context-gap posts make educational arguments. The underlying conviction is consistent, but buyers should ask for evidence supporting the endpoint-native claim beyond its promotional packaging.
Agents could be how small companies compete — if someone governs them
Ajay steps back from controls to argue that agents matter most for smaller businesses.
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The shift is from AI as a copilot that helps people write, research, or answer faster to agents that take a goal, use a browser and other tools, reach into business systems, learn workflows, and carry out multi-step work.
The consequence is economic: a small company historically could not afford dedicated teams for every function, such as go-to-market, data analysis, lead generation, sales, or ad management, and agents could change that equation.
Ajay pairs the opportunity with governance: when every SMB has dozens of agents acting across SaaS applications, company data, and the internet, the questions become what those agents can access and what actions they are allowed to take.
Ajay's formulation is that a 20-person company may soon operate with some of the capabilities of a 200-person company, not because people disappear but because each employee can have a fleet of specialized digital workers.
This is the one post about another company's product discussion rather than Ajay's own analysis or Kitecyber's roadmap.
Ajay credits the conversation with Howie Liu of Hyperagent as the source of the direction described.
The governance argument that follows is Ajay's framing, connecting this optimistic post to the more cautionary agent-security material elsewhere.
One post moves outside endpoint and agent themes to argue that internal security is not enough: an attack on a software or service supplier can hit customers hard.
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Buyers should check that their suppliers are paying attention to security and compliance before an incident in someone else's infrastructure becomes an operational outage.
Ajay points to a ransomware attack on Blue Yonder and its effect on grocery chains across the US and UK as an illustration of supply-chain exposure.
This argument stands apart from the agent-focused material that dominates the rest of the corpus: it reads as a more general cybersecurity concern and an earlier line of thought, rather than part of the endpoint enforcement thread.
This is Ajay's clearest statement of their core argument: the cybersecurity industry has a complexity problem, and most small and mid-sized businesses need fundamentals plus integration, not dozens of tools.
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Start here to understand the priorities behind their other writing.
Ajay opens with a customer question — "What is the minimal set of tools I need to feel secure and meet my compliance needs" — and argues that SMB security has become needlessly complicated.
The post lists ten practical steps: know what devices, users, and applications you have; enforce multi-factor authentication everywhere; patch operating systems and applications; protect endpoints; back up critical data and test the backups; limit access to what each person needs; monitor for unusual activity; train people continuously rather than once a year; document lightweight policies; and keep assessing and improving.
The real difficulty, Ajay says, is that most businesses try to cover these needs with separate point products.
They point to Kitecyber as a unified alternative that combines visibility, protection, monitoring, and compliance.
For brands planning a campaign
The ten steps work as a ready-made webinar outline or assessment checklist. A partner could present the sequence and invite Ajay to explain where SMBs usually stall, with Kitecyber positioned as one way to reduce tool sprawl.
This short post draws the line between compliance and actual data protection, a recurring theme in Ajay's work.
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It helps buyers assess whether an audit badge is being treated as proof of security.
Ajay argues that a SOC 2 Type II audit can be passed while sensitive data still leaves the organization every day.
The post attributes this to scope: the tested controls did not cover the route the data actually took — the endpoint, the browser, or a generative AI prompt.
Ajay says the post will cover which controls to revisit and what should change.
For brands planning a campaign
This is a strong hook for a joint webinar or article on the gap between audit scope and real data movement. A compliance or security partner can bring framework expertise while Ajay brings the endpoint and AI-agent angle.
Ajay explains why existing security categories each see only part of an AI agent's action.
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The post introduces the specific problem they are building around.
Ajay describes an AI agent accessing files, using SaaS applications, calling APIs, browsing the web, and acting for a user in one sequence.
Each existing tool sees a fragment: endpoint detection sees the process, data loss prevention sees the data, and the secure web gateway sees the connection.
None sees the whole action.
Ajay links to a Slashdot article on the context gap and argues that the CISO's question is no longer just which AI security tool to add, but whether existing controls can correlate user, device, application, data, process, and network activity at the point where the action happens.
For brands planning a campaign
Useful for a panel or technical webinar that follows a single agent's actions and shows where each control loses visibility. It gives a partner a concrete sequence to demonstrate.
This is Ajay's most concrete operational framing of the AI-agent security problem, spelling out what a control has to do in the moment rather than after the fact.
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Read it for the four action requirements that anchor their product thinking.
Ajay opens with an AI agent making 400 decisions about company data with zero approvals, under a user's login and at machine speed.
The post references TypeSafe's Jev model, described as returning a typed decision in well under a second, and contrasts that pace with data loss prevention tools that mostly react after data moves.
Ajay lists four requirements: classify data the moment an agent touches it on the device; know whether a person acted or an agent acted as that person; allow, warn, or block before anything leaves; and keep tracking the data after it is converted, encoded, or screenshotted.
The post frames the contest as agent speed versus security speed.
For brands planning a campaign
This post can anchor a technical demo or benchmark session. A partner could build a live scenario showing how quickly an agent touches data and where a slower control would miss the action.
Shows how Ajay presents a commercial launch and uses market statistics to justify it: near-universal AI adoption alongside missing access controls.
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Useful for understanding the claims and proof points they bring to a partnership.
Ajay announces USA Today coverage of Kitecyber's AI Workflow Security launch.
The post cites a Stanford 2026 AI Index figure of 88% organizational AI adoption, a Microsoft finding that 46% of leaders say their organizations already use agents to fully automate processes, and an IBM 2025 figure that among organizations with an AI-related security incident, 97% lacked proper AI access controls.
Ajay argues that the security question has shifted from who has access to whether a specific user or agent should move a specific piece of data right now, and that the decision belongs at the endpoint where the data actually moves.
The product governs read, copy, transform, upload, and share attempts by humans, copilots, and autonomous agents, and ships through the same lightweight agent as Kitecyber's existing data loss prevention, endpoint management, secure web and SaaS access, and zero-trust private access.
For brands planning a campaign
A launch-style partnership could use the adoption-versus-controls contrast as its opening argument, then move into a joint demonstration of the endpoint-native process. The cited third-party statistics give a partner an easy shared frame.
This post shows Ajay acting as an industry commentator, not just a product marketer.
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It reframes the evaluation question for security buyers.
Ajay shares a SourceForge article on evaluating data security in the age of AI agents and argues that most data loss prevention was designed for humans.
AI agents can reach files, SaaS apps, email, browsers, APIs, and sensitive data using legitimate user permissions, making their activity look normal to existing tools.
Ajay says the operative question is shifting from whether sensitive data moved to who or what moved it, why, and whether the action should have been allowed.
Answering that requires context across user, device, application, process, data, and network.
The post asks security leaders how they evaluate data security solutions today.
For brands planning a campaign
A good basis for a co-authored evaluation guide or webinar: a partner can walk through the questions while Ajay explains what the answers should look like when AI agents are in the environment.
Passing compliance is not the same as protecting data3
Simplifying security for small and mid-sized businesses2
AI agents break the assumptions behind data loss prevention2
The context gap: no single control sees the whole action2
Endpoint-native enforcement delivered through one agent2
The agent economy as a competitive lever for small companies1
Third-party and supply chain exposure1
Simplifying security for small and mid-sized businesses
The most consistent argument across the corpus is that small and mid-sized companies are buried in overlapping security tools and cannot staff their way out.
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Ajay opens one post with a customer question — "what is the minimal set of tools I need to feel secure and meet my compliance needs" — and answers it with a ten-step fundamentals list: inventory what you have, enforce MFA, patch, protect endpoints, back up and test recovery, apply least privilege, monitor for unusual activity, train people continuously, document lightweight policies, and keep assessing. A separate post names the same pressure directly: modern SaaS, GenAI tools, coding agents, browser extensions, and cloud services running on "SMB-grade staffing," producing too many tools, too many tickets, too little time, and too much risk.
Opens with a customer question, then answers it with a numbered framework.
For brands planning a campaign
A joint webinar on cutting tool sprawl and covering the fundamentals fits material Ajay already publishes.
The ten-step list offers a usable agenda or downloadable checklist, and the "minimal set of tools" question is a ready-made opening hook.
An enterprise-scale stack pitch would run against their argument.
AI agents break the assumptions behind data loss prevention
A large share of the recent material argues that agent behaviour invalidates the assumptions behind DLP.
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Ajay contrasts agents making hundreds of decisions under a user's login at machine speed with controls that react after data moves, and asks what fast decision-making means for security tooling.
A related post on evaluating data security argues that agent activity through files, SaaS apps, email, browsers, and APIs often looks normal to existing tools, shifting the operative question from "did sensitive data move?" to "who or what moved it, why, and should that action have been allowed?"
A third post makes the human-versus-agent framing explicit: the race is agent speed against security speed.
For brands planning a campaign
This is the closest match to the proposed webinar on endpoint AI security.
The "who or what moved it, and should that action have been allowed" question is a strong framing device for a joint session.
A post tied to a SourceForge article also shows Ajay already discusses agent data security in third-party editorial contexts, not only in product announcements.
Endpoint-native enforcement delivered through one agent
Several posts describe Ajay's product direction in concrete terms: a single lightweight endpoint agent carrying DLP, endpoint management, secure web and SaaS access, and zero-trust private access, with no gateways, appliances, or new infrastructure.
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One launch post cites external adoption statistics and states that the controls are nearly absent despite near-universal adoption. A longer post lists unified endpoint management capabilities — automatic enforcement of compliance policies, one agent for onboarding and offboarding, cloud-native operation without Active Directory, and pre-built compliance templates.
For brands planning a campaign
The material describes the delivery model in detail, giving a webinar room to examine deployment and operational trade-offs rather than stay conceptual.
It is also closely tied to promoting Kitecyber's platform.
For a partnership, set an explicitly educational frame and ask a co-host to press on limitations, not just amplify the launch narrative.
Passing compliance is not the same as protecting data
Ajay argues that audit scope and data-protection reality can diverge.
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One post states plainly that a company can pass a SOC 2 Type II audit and still leak data daily because the endpoint, browser, and GenAI prompt where the data actually left were never in scope; it promises a walkthrough of which controls to revisit and how evidence should change. Elsewhere, the same theme appears from the other side: lightweight documentation and continuous assessment as compliance fundamentals, and automatic generation of audit-ready evidence as a product feature.
For brands planning a campaign
This is a distinct, productive webinar angle: how AI-era data movement changes what auditors should test and what evidence should look like. It is less saturated than generic AI-security messaging and gives the co-host a concrete discussion to lead.
The agent economy as a competitive lever for small companies
One post steps back from security controls to argue that agents matter most for smaller businesses.
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Ajay describes the shift from copilots to agents that take goals, use browsers and business systems, and carry out multi-step work.
They argue a twenty-person company could soon operate with some of the capabilities of a two-hundred-person company because employees gain fleets of specialised digital workers.
Ajay pairs that opportunity with governance questions: what those agents can access and what actions they are allowed to take.
This is Ajay's only post here built around another company's product discussion rather than their own analysis or Kitecyber's roadmap.
For brands planning a campaign
This offers a broader, more forward-looking topic than endpoint controls alone and suits a strategic opening segment about where SMB adoption is heading. It is industry commentary rather than hands-on guidance, so use it to frame the session rather than as its operational core.
The corpus includes a narrower but clearly stated argument: being secure internally is not enough when suppliers are compromised.
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Ajay notes that attacks on a software or service supplier can hit customers hard and points to a ransomware incident affecting grocery chains across the US and UK, urging buyers to check that their suppliers pay attention to security and compliance.
This is the only post here that moves outside endpoint, AI-agent, and compliance themes. It reads as an earlier, more general cybersecurity concern than the later agent-security material.
For brands planning a campaign
Useful as a secondary webinar angle or a panel segment on supplier assurance, this also shows Ajay engages with risk categories beyond endpoint tooling. Because it is a single post, ask for additional examples before building a standalone program around the topic.
Posts speak as part of the Kitecyber team, describing endpoint security products and launches the company builds.
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Other company names appear as cited research or news examples rather than as commercial relationships. The posts carry no sponsorship, affiliate, or partnership disclosures.
Kitecyber
Employment
Posts speak from inside Kitecyber, using “we” and “our team” for products the company builds, including the AI Workflow Security launch, Kitecyber UEM endpoint management, and the argument that data security should be enforced at the endpoint.
Points readers to a SourceForge article on evaluating data security in the age of AI agents and shares a first-person view on how agent activity changes DLP assumptions.
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The post does not state an authoring, commercial, or sponsorship relationship with SourceForge.
Comments on a public conversation with Howie Liu of Hyperagent about where AI agents are heading and what agent fleets could mean for small businesses.
Ajay G. writes mainly on LinkedIn about simplifying cybersecurity for small and mid-sized businesses and how AI agents change endpoint data security.
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Their posts cover a customer's question about a minimal tool set with ten fundamentals, agent-speed decisions, the context gap between endpoint detection, data loss prevention and secure web gateways, and why passing a SOC 2 Type II audit is not the same as stopping data leaks.
Promotional material on unified endpoint management and AI workflow security shows the product direction behind these arguments.
A post on the agent economy and one on supplier risk broaden the range of concerns they address.
Anne Bisagno and Ajay G. write LinkedIn posts for business and security leaders, translating topics such as BYOD, device trust and zero trust into governance and resilience arguments rather than purely technical briefings.
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Both connect security choices to hybrid work, cloud adoption and compliance expectations.
Anne Bisagno centres on BYOD policy, device trust models and phased zero-trust adoption, often directing readers to a fuller article. Ajay G. focuses more on endpoint-native enforcement, agent-driven data movement and reducing tool sprawl for small and mid-sized businesses, with a stronger product-direction thread.
Cole McMillan and Ajay G. write practitioner-facing posts about running security and IT operations with constrained staffing, covering identity, endpoint management, compliance, automation and Microsoft-centred environments.
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Both use operational examples rather than abstract policy language.
Cole McMillan details specific deployments and operating models, including a Microsoft 365 and Intune migration and a single-person IT function supporting 150 users, with ticket and response-time metrics. Ajay G. more often argues from a vendor or product-strategy perspective about governing endpoint security and AI-agent data risk, writing for a broader SMB audience.
Arvind Suthar and Ajay G. publish educational security content about compliance and data protection in Microsoft environments, covering phishing-resistant MFA, patch cadence, DLP configuration and Copilot data readiness.
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Both explain regulatory and control frameworks for practitioners, not just announce products.
Arvind Suthar focuses on GCC High and CMMC implementation for regulated and defense-adjacent organisations, drawing on a published handbook, control mappings and government-cloud constraints. Ajay G. focuses on small and mid-sized business security, endpoint enforcement and AI-agent data risk, with a broader commercial product narrative.
For a webinar aimed at endpoint-AI-security buyers, one number is available, not an audience profile.
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Ajay G.'s LinkedIn profile had 14,610 followers on 8 October 2026 — the only measured audience figure published in the posts here.
The posts here do not say how those followers break down by industry, job function, seniority, company size, or location, so how well the audience matches a cybersecurity buyer cannot be measured from what is here.
The 10 posts here do show who the writing is aimed at: owners and IT, security, or compliance leads at small and mid-sized companies who are running modern SaaS and AI tools with thin staffing.
That is content appeal — a read on the intended reader — not a claim about who actually follows or sees the posts.
Treat it as the hypothesis to confirm with the creator rather than a finding.
Two practical cautions: a follower snapshot does not show how many people see or act on a post, and the count is a single point in time rather than a trend.
Next step before committing to a joint webinar: ask them for LinkedIn follower analytics — industry, seniority, function, company size, and location — plus recent post impressions and engagement, and registration or attendance figures from any webinar or live session they have run.
Those three sets of numbers answer the placement question; the follower count alone does not.
The LinkedIn posts show Ajay G. promoting Kitecyber’s own products: a launch of AI Workflow Security that received USA Today coverage, a unified endpoint management product, and a single lightweight agent that handles DLP, endpoint management, secure web and SaaS access, and zero-trust private access.
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Posts also point readers to third-party coverage in SourceForge and Slashdot and invite them to get in touch about IT, security, and compliance simplification.
The posts don’t state rates, deliverables, editorial terms, booking lead times, sponsor approval steps, or who handles partnership requests, so ask Ajay G. directly about the operational terms.
They show a clear subject lane a partner could build on: AI agents acting under user credentials, DLP that reacts only after data moves, endpoint-level visibility, and compliance for resource-constrained SMBs.
Treat these as topic context, not a confirmed offer to collaborate.
A security vendor selling endpoint and AI-agent data protection to small and mid-sized companies is a strong match for a thought leadership partnership and a joint webinar on endpoint AI security.
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Ajay G. has already established the subject lane.
Across these posts, they argue that AI agents read files, call tools, and move data under a user's own login at machine speed, while most data-loss-prevention tools react only after data has left.
One post frames the problem this way: EDR sees the process, DLP sees the data, and the secure web gateway sees the connection, but no control sees the whole action.
Another reduces the goal to "See.
Decide.
Enforce.
Continuously" and lists four requirements: classify data the moment an agent touches it on the device, distinguish a person from an agent acting as that person, allow, warn, or block before anything leaves, and keep tracking data after it is converted, encoded, or screenshotted.
That makes a natural webinar agenda.
The format fits, too.
They teach through numbered frameworks, and the ten-step SMB security list in these posts could anchor a session or downloadable checklist.
Their writing targets owners and IT, security, or compliance leads at companies running modern SaaS and AI tools with thin staffing.
They have also discussed agent data security in third-party editorial contexts, linking to SourceForge and Slashdot coverage, making co-hosted editorial a natural next step beyond product announcements.
Plan for two boundaries.
Several posts promote Kitecyber directly, so frame the session explicitly as educational and choose a co-host who can press on limitations rather than amplify a launch narrative.
Sponsorship terms, deliverables, and rates are not published; ask directly about commercial terms before announcing anything.
When to choose a specialist
A Fortune 500 identity-governance vendor targeting CISOs at large banks with a multi-touch creator program is a poor match.
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The campaign calls for short-form video, a paid newsletter takeover, benchmark-style product comparisons for enterprise security teams, guaranteed reach, and a published rate card with lead times.
Ajay G.'s published content offers little support for that brief.
The channel is the first problem.
These posts are all short LinkedIn text.
There is no video, podcast, or long-form article content, so there is no basis to assume they can carry a video-led program or newsletter takeover.
Budgeting for formats they have not been observed using would be a gamble, not a plan.
The audience is the second problem.
The only published audience figure is 14,610 LinkedIn followers; there is no breakdown by industry, seniority, function, company size, or location, and no impressions or engagement data.
That is not enough to validate a promise of reach to enterprise bank CISOs.
The posts point instead to small and mid-market companies.
One names the pressure of running modern SaaS, GenAI tools, coding agents, and cloud services on "SMB-grade staffing."
The ten-step post explicitly rejects the enterprise-scale stack pitch, arguing that most SMBs do not need fifty tools or a security doctorate.
The commercial fit is also unproven.
No sponsorship page, media kit, rate card, or partnership policy is published.
Ask directly about availability, deliverables, lead times, and approval steps before considering the campaign.
A program aimed at CISOs at large banks would also run against their argument that smaller buyers should consolidate tooling rather than add enterprise-grade complexity.
A worked campaign example
For an endpoint and AI-agent data-security vendor seeking a thought leadership partnership anchored by a joint webinar on endpoint AI security, here is a plan.
Premise. Open with the problem Ajay G. already covers. An AI agent can open files, call APIs, browse the web, and act on a user's behalf inside a single workflow, while each control in a typical stack sees only a fragment. Ask a question any security lead can answer in one sentence: who or what moved this data, why, and should that action have been allowed? This vendor-neutral framing opens the session before any product discussion and sets up the harder question of where enforcement should sit.
Format and length. Host one 45-minute live session with two presenters and no more than eight slides. Keep it conversational; these posts are short, not keynote decks. Record it and cut the agent-speed segment into a short clip.
Agenda. First, cover the context gap, using the EDR, DLP, and secure web gateway illustration to show why no single control sees the whole action. Second, examine agent speed versus security speed through the four enforcement requirements: classify at the moment of the touch, distinguish person from agent, block before data leaves, and keep tracking after conversion or encoding. Third, address the audit-scope gap: data can still leave through the endpoint, browser, or a GenAI prompt after a SOC 2 Type II audit because those routes were never in scope. Close with a short list of questions buyers should ask any data-security vendor.
Working asset. Turn the ten-step SMB security list into a one-page downloadable checklist. Because the post containing the list ends with product positioning, have the co-host present the steps as fundamentals and save the vendor's tooling for the closing Q&A rather than building it into the framework.
Measurement. Agree on registrations, attendance rate, and post-session asset downloads before launch. Ask Ajay G. for an audience breakdown and post engagement figures to assess how well their following maps to the target buyer.
Open items before signing. Confirm whether a paid partnership is available and on what terms; whether Ajay G. will present on camera or only in audio; and what approval steps apply to edits of the recording and the co-branded checklist. A co-host who can press on the limits of endpoint enforcement will make the session more credible than one who only amplifies a launch narrative.
Choose Anne Bisagno over Ajay G. when a webinar targets leadership and governance rather than security engineering.
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Bisagno's two Posts frame BYOD and device trust as business resilience and governance initiatives, not IT decisions.
They argue that identity and device posture—not network- and password-centric controls—reduce risk in hybrid work.
A vendor selling an endpoint agent to regulated industries, with CIOs, risk owners, or HR-adjacent leaders as buyers, will find a natural fit: the session can focus on policy, employee experience, phased Zero Trust adoption, and how to start without rebuilding infrastructure.
That is a different entry point from Ajay G.'s machine-speed agent problem, and an easier one to defend to a compliance-driven audience.
The tradeoff: Bisagno's material does not address what happens when an AI agent reads files, calls tools, and moves data under a user's login.
A joint session would cover device trust and BYOD governance, but Ajay G.—not Bisagno—makes the case that existing DLP reacts after data leaves and that enforcement must sit at the endpoint where the action happens.
Expect to lose the agent-speed hook and the technical enforcement detail a product-led webinar usually needs.
Covers device trust, BYOD policy, and phased Zero Trust governance, but not agent-driven data movement or endpoint enforcement at machine speed.
Choose Cole McMillan over Ajay G. when the campaign needs a documented, hands-on operating model rather than a threat framing.
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McMillan's Posts describe a full Microsoft environment build: a migration from reseller-managed Microsoft 365 licensing to a direct CSP model; a standardized identity-attribute scheme feeding dynamic groups; Intune enrollment; BitLocker, Defender, firewall, and update rings; Windows LAPS; and Conditional Access policies deployed in report-only mode before enforcement.
One post details a single admin running IT for 150 users across multiple sites without a managed service provider, with stated results: roughly 4x ticket volume from centralized intake, about 90% faster first response, and about 60% less time to resolution.
If the campaign needs to explain how a small team actually runs IT, McMillan's firsthand numbers and build sequence answer the question that Ajay G.'s Posts mostly raise.
The tradeoff: McMillan's content centers on the Microsoft stack and IT operations, not agent data security or AI-specific DLP.
A webinar with McMillan would lean toward identity, licensing, and endpoint configuration for Microsoft-centric administrators, rather than the vendor-neutral who-moved-this-data argument that makes Ajay G. a fit for an AI-agent data-protection sponsor.
Buyers who do not run Microsoft 365 as their core platform will find less to use, and the session's product story shifts from data enforcement to environment governance.
Strong firsthand operating detail on Microsoft 365, Entra ID, and Intune, with measurable outcomes, but focused on that ecosystem and IT operations rather than AI-agent data protection.
Choose Arvind Suthar over Ajay G. when a webinar targets government, defense industrial base, or heavily regulated buyers whose purchases are driven by compliance obligations.
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Suthar's Posts show a sustained practitioner publishing practice: a continuously updated "Securing GCC High" reference, now described as 793 pages, with release notes covering a 75-check security assessment across six pillars mapped to CMMC, HIPAA, CJIS, and SOC 2; a full CMMC control set; Conditional Access policies mapped to each 800-171 control; phishing-resistant MFA and patch cadence; and Purview data-readiness before turning on Copilot in GCC High.
The CMMC Phase 2 pause post argues that hygiene controls cost the same with or without controlled unclassified information and separates basic hygiene from the expensive compliance tail.
For a sponsor whose buyers must show an assessor evidence, Suthar's control-mapping practice and free documentation asset are a stronger draw than a general SMB pitch.
The tradeoff: the material stays within the GCC High and CMMC lane, so a sponsor targeting ordinary small and mid-sized businesses will reach a narrower audience, and the conversation centers on compliance rather than agents acting under a user's login.
Ajay G. reaches non-regulated SMB and mid-market buyers with the agent-risk and audit-scope argument; Suthar reaches regulated buyers with control evidence and documentation.
Pick based on which audience the campaign is paying to reach.
Deep GCC High, CMMC, and Microsoft compliance guidance with audit-ready control mapping, but focused on regulated public-sector buyers rather than the general SMB and mid-market audience.
This page draws on ten LinkedIn posts by Ajay G. and the public profile, which showed 14,610 followers on 8 October 2026.
The posts cover SMB security fundamentals, unified endpoint management, AI-agent data risk, the limits of traditional data loss prevention, compliance and audit scope, agent-economy commentary, and software supply chain risk.
For comparison, two posts each by three creators working in adjacent territory cover Anne Bisagno on BYOD policy and phased zero trust adoption, Cole McMillan on Microsoft-centred IT and security operations with lean staffing, and Arvind Suthar on government-cloud and compliance implementation.
Those posts were dated between April and September 2026.
Ajay G.'s own posts do not show publication dates, so this page describes positions and recurring arguments rather than a dated timeline, and it does not claim how the views changed over time.
The posts discussed here are written LinkedIn posts only.
They do not include video, podcast, or long-form article content, so delivery in those formats is unverified, and no sponsorship page, media kit, rate card, or partnership policy is published in the posts here.
Nothing here should be read as a finding that no sponsorship offer exists; it means none is mentioned in the posts here.
The posts here do not give an audience breakdown by industry, seniority, function, company size, or location, and they do not publish impressions or engagement data.
One gap stays open.
Ajay G.'s posts here do not establish signature books, shows, earlier career history, or other work that people may have in mind when they search this name.
If those exist, they are not established by the posts here, and the opening of this page should be revisited once they are.