Shashi Bellamkonda

People get flagged when they change roles; the tools they wired into company systems do not.

• They recommend four checks before anything goes live: who owns it by name, what it can call, its spend ceiling, and who can shut it off—and how fast.
• The reasoning is procedural, not technical: if finding the owner takes a meeting, the bill arrives first.

A hypothetical support agent connects to a customer database.

• When its builder moves to a new team, HR flags the analyst’s access change but not the agent.
• Bellamkonda also reports a Mandiant account from the Cyber Defense Summit: one agent made more than 15,000 high-cost calls in under an hour, running up roughly $50,000 in cloud charges, with no attacker involved.
• They walk through ManageEngine’s SecureAI release, which puts agents from four platforms on one list, assigns owners to ownerless agents, schedules access reviews and screens tool requests through a gateway.
• They note one limit: agents on other platforms never appear on the list.

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An agent runs inside the perimeter with credentials and a budget.

• Nobody reviews a colleague’s every call, so nobody reviewed the agent’s.
• The point is about behaviour: once something is sanctioned, scrutiny drops.
• They put the conclusion plainly: an agent without a strategy is an insider with your credit card and no supervisor.

The runaway agent that made more than 15,000 high-cost calls in under an hour belonged to the company, not an attacker. Bellamkonda uses the incident to make the case for a pre-production review, not another trend summary.

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They lay out both sides, then press vendors for commercial answers in writing rather than accepting a keynote as a plan.

• Their recurring questions: what does a typical task cost, and how do skills and memory come back if the customer leaves?
• Their test is whether the answer holds up in writing.

Google Cloud’s Gemini agent, announced at Gemini at Work 2026, gets its own mailbox, calendar and Drive account.

• Administrators can set a hard per-project spending limit that pauses the agent at the cap.
• Bellamkonda notes that Google published the cap but no unit price to set it against, while Microsoft, Amazon, OpenAI and Salesforce each publish a billing unit.
• They ask Google Cloud for the cost of a typical agent task and the export path before a team builds on it.
• They make the same point about Microsoft and NVIDIA’s local-AI hardware, ending with three questions for Microsoft: which console manages local model deployment across the fleet, how sandbox policy is defined, and how model versioning and updates work.

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When a vendor scores its own work, they say so and call the result a vendor claim until an outside party repeats it on its own code. Naming the alternatives matters because those are what the number would replace in practice.

Cisco’s Antares scores 0.823 at finding bugs in code a team can read and 0.000 once the program is built, according to Cisco’s own tests.

• Bellamkonda notes that the 0.823 belongs to Antares-3B, which is still coming soon, while the downloadable models score 0.756 and 0.604.
• On Cisco’s harder 500-bug test, 190 bugs beat every model tested. gpt-5.5 scored 0.857 on the same source test but sends code to a cloud service.
• Rule-based scanners and human analysts remain on the list, too.
• They credit Cisco’s Amin Karbasi, whom they say they first heard at Black Hat in August.

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The setup no longer requires a complex Linux installation, widening the number of teams that can try it.

• The question they keep returning to is what happens after the first device works: how to manage policy and model versions across a fleet.
• That is the next constraint.

The Microsoft and NVIDIA RTX Spark announcement includes a Surface Laptop Ultra and a Surface RTX Spark Dev Box.

• It splits hardware into a 24GB tier for smaller models and a 128GB tier for high-context workloads, naming Hermes Agent and Microsoft Execution Containers as tools to simplify setup and sandbox management.
• Bellamkonda says the price remains high and they will be watching for fleet-console capabilities for policy and model updates.

Bellamkonda writes that they long hesitated to run local models because of setup complexity and hardware cost. They say the announcement changed their view, while retaining the cost objection in the same post.

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Start here for a concrete staffing scenario that illustrates the governance problem and a specific product response, including its platform limits.

Shashi Bellamkonda opens with a practical hypothetical: an analyst builds a support agent, connects it to the customer database, then moves to a new team.

• HR flags the analyst’s access change, but the agent never gets flagged.
• The post says ManageEngine released SecureAI on October 6 to close that gap.
• They describe an early briefing and analysis: SecureAI finds agents built on Microsoft Copilot Studio, Microsoft Foundry, Zoho Zia, and Google Gemini Enterprise Agent Platform; assigns an owner to each agent that has none; runs access reviews on a schedule; and screens tool requests through a gateway.
• The post states one limitation plainly: agents on any other platform do not appear on the list.
• ManageEngine offers a 45-day free trial with no credit card.

For brands planning a campaign

Useful for a webinar or article series on managing AI agent access and spend.

• A demand-generation piece could build on the post’s audit question: Which agents run, who answers for each, and what can each agent reach?
• The platform limitation also gives buyers with mixed estates a natural discussion point.

It gives a specific, memorable failure and turns it into four operational questions readers can apply immediately.

Bellamkonda reports a Mandiant, part of Google Cloud, example from the Cyber Defense Summit: one AI agent made more than 15,000 high-cost calls in under an hour and ran up about $50,000 in cloud charges.

• No attacker was involved; the agent belonged to the company.
• The post argues that internal, approved tools can be more dangerous because teams relax around them, and an agent sits inside the wall with credentials and a budget.
• Bellamkonda says they use agents, but adds: “an agent without a strategy is an insider with your credit card and no supervisor.”
• The post lists four checks before production: who owns it by name, what can it call, what is its spend ceiling, and who can shut it off and how fast.

For brands planning a campaign

A strong source for a campaign on agent identity, spend controls, and incident response. A B2B SaaS vendor could turn the four checks into a webinar outline, an assessment checklist, or a short demand-generation series on stopping runaway agents before production.

Start here for a clear picture of Google’s announcement and the commercial questions buyers should ask before committing work to the agent.

Bellamkonda describes Google Cloud’s Gemini agent, announced on October 8 at Gemini at Work 2026.

• Give it a goal, such as a slide deck on last quarter’s sales, and it plans work, pulls data from systems, and returns output in Gmail, Docs, Slack, or Microsoft 365.
• The post highlights that a coworker agent gets its own email address, calendar, and Drive account, and colleagues can add it to a chat space like a teammate.
• Administrators can set a hard spending limit per project, and the agent pauses when it reaches the cap.
• One policy written once applies through a gateway.
• The post also cites a Bloomberg Media report of a 63% gain in query accuracy during early development, according to Google’s keynote.
• It asks Google Cloud for two answers in writing: the price of a typical agent task, and how skills and memory are exported if a customer leaves.
• Google had not stated availability or pricing at the time.

For brands planning a campaign

Useful for a webinar or article comparing agent identity and cost controls across providers. The two written questions for Google, alongside the contrast with billing units from Microsoft, Amazon, OpenAI, and Salesforce, could anchor a procurement-focused demand-generation asset.

It lays out the strong claim, the version distinction, who ran the tests, and the alternatives.

Bellamkonda explains Cisco’s Antares, a small AI model that scores 0.823 at finding bugs in code a team can read, but 0.000 once the program is built.

• The model names the files to open first; the post compares it to a sniffer dog at an airport, where the officer still decides.
• Bellamkonda says they first heard about the work from Cisco’s Amin Karbasi at Black Hat in August.
• The post notes that Antares is small enough to run on your own machines, keeping source code in-house, and that two versions are free on Hugging Face.
• It separates claims from shipping reality: the 0.823 belongs to Antares-3B, which was still coming soon, while downloadable models score 0.756 and 0.604.
• On Cisco’s harder 500-bug test, 190 bugs beat every model tested.
• Cisco built and scored the tests itself.
• Alternatives include rule-based scanners, hosted AI models, and human analysts.
• Treat the score as a vendor claim until someone outside Cisco repeats the test.

For brands planning a campaign

Useful for demand-generation content on evaluating AI security claims, on-premises versus cloud code analysis, and practical AppSec workflows. A vendor or agency could build an article or webinar around reproducing vendor benchmarks and choosing tools for teams that cannot send code to a cloud service.

Start here for a practical pilot checklist that goes beyond hardware specs to fleet management, sandbox policy, and update ownership.

Bellamkonda says they long hesitated to run local models because of setup complexity and hardware costs, but the Microsoft and NVIDIA RTX Spark announcement marks a shift: local AI no longer requires a complex Linux installation, which they call a major win for IT teams.

• The Surface Laptop Ultra and Surface RTX Spark Dev Box were launching soon, though the price was still high in their view.
• The post distinguishes a 24GB tier for smaller models from a 128GB tier for high-context workloads, and mentions Hermes Agent and Microsoft Execution Containers as tools that simplify setup and sandbox management.
• Bellamkonda says the next frontier is centralized manageability and that they will watch for fleet console capabilities for policy and model updates.
• For an AI pilot, the post suggests three questions for Microsoft: which console manages local model deployment across the fleet, how sandbox policy is defined, and what the process is for model versioning and updates.

For brands planning a campaign

Useful for demand-generation content aimed at IT leaders planning local AI pilots. The three questions could become a downloadable checklist, a webinar segment, or a vendor-neutral buying guide on manageability beyond the first device.

A Mandiant account of one agent making more than 15,000 high-cost calls in under an hour and running up roughly $50,000 in cloud charges anchors the argument: an agent sits inside the firewall with credentials and a budget, but nobody reviews it as they would a colleague.

• ManageEngine's SecureAI announcement makes the same case: every agent gets a named owner, an access-review schedule, and a gateway that can block or mask a tool request.
• Buyers should ask whether agents on platforms beyond the four supported ones are covered; those agents do not appear on the list.
• The Google Cloud Gemini agent post makes the point from the product side: a coworker agent can have its own mailbox and calendar, but a named human owner answers for it from day one.

Each post answers a governance question with a short list a buyer can act on: who owns the agent by name, what it can call, its spend ceiling, who can shut it off, and how quickly — rather than a framework recommendation.

The posts treat agent governance as an ownership, access, and budget problem rather than a regulatory-compliance exercise. They name no regulatory frameworks.

For brands planning a campaign

A vendor selling agent observability, identity, or cost controls can build a webinar or article series around these questions and the $50,000 spend anecdote. The four questions work as a downloadable pre-production checklist, a webinar poll, or the spine of a demand-generation sequence for IT and security leaders who already have agents running.

Examples: Source 1 · Source 2 · Source 3

The Microsoft and NVIDIA local-AI post focuses on setup complexity and hardware cost, notes that the price is still high, and ends with three questions for Microsoft about fleet management consoles, sandbox policy, and model versioning.

• The Google Cloud Gemini agent post separates what changes (a team can delegate work to an agent with its own identity and an audit trail naming it) from what does not (someone still answers for it).
• It notes that Google published a spending cap but no unit price to compare against, while Microsoft, Amazon, OpenAI, and Salesforce each publish a billing unit.
• The post advises buyers to get two answers in writing — the price of a typical task and how skills and memory export — before building work on the platform.

Announcements are summarised in plain terms, marked as keynote claims when availability or pricing is unstated, and turned into specific written questions for the vendor.

For brands planning a campaign

This format suits a vendor seeking to be seen as a straight-talking evaluator rather than a promoter. A SaaS company could commission the same treatment for its own category — what changes, what stays the same, and what to ask in writing — or host a session where unanswered questions set the agenda.

Examples: Source 1 · Source 2

The Cisco Antares post reports a model that scores 0.823 finding bugs in readable code and exactly 0.000 once the program is built.

• It names the alternatives — rule-based scanners, hosted models, and human analysts — flags that Cisco built and scored its own tests, distinguishes the coming 3B model from downloadable versions that score lower, and concludes that until someone outside Cisco repeats the number on their own code, it remains a vendor claim.
• The Mandiant post turns an incident account into Monday-morning checks rather than a trend summary.

Claims are paired with supporting numbers, the test source, alternatives a buyer might consider, and the conditions under which the claim should not be trusted.

For brands planning a campaign

For a security vendor, this is a model of credible third-party analysis: a named claim, its limits, and a test the reader can run. It fits a sponsored research explainer, an AppSec webinar, or a comparison asset that includes competitors honestly — a differentiator in a category full of single-vendor benchmarks.

Examples: Source 1 · Source 2

It notes that setup no longer requires a complex Linux installation, distinguishes hardware tiers for smaller models from those for high-context workloads, points to sandbox and setup tooling, and identifies centralized fleet management for policy and model updates as the next open question. It also says the cost is still high.

Hardware and platform news is assessed in terms of what an IT team would have to operate day to day, with unanswered operational questions framed as questions for the reader to ask.

For brands planning a campaign

A hardware or platform vendor targeting enterprise IT can use this framing for a pilot-readiness campaign: the three questions about consoles, sandbox policy, and version updates map to a pilot checklist or assessment offer. It also supports articles for teams weighing on-premise against hosted models on cost and control.

Examples: Source 1

Each opens with a specific number, incident, or personal observation; explains a product or finding in plain language; separates what changes from what stays the same; names alternatives or limitations; asks the reader a direct question; and points to a longer analysis.

• Topics recur — agent ownership, spend limits, access reviews, and the gap between a vendor's own benchmark and independent verification — while the format stays consistent enough for a brand to anticipate what a sponsored piece would look like.
• Each post credits the event, briefing, or person behind the information.

Bulleted, plain-language capability lists; an explicit what-changes/what-stays-the-same split; a closing reader question; and a link to the full analysis instead of the whole argument.

For brands planning a campaign

A brand can reuse the format as a content template: a short social post carrying the decision, with the full analysis hosted or gated on the sponsor's site. The closing question can drive comment-based demand generation, while the framework items translate into webinar segments or an email series.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

The governance post says an early briefing was received and that both capabilities and current limitations were analysed.

Attribution to the event, briefing, or named individual, with limitations covered alongside capabilities rather than only announced features.

For brands planning a campaign

Briefing-led access lets a vendor shape coverage by providing early, detailed information with trade-offs acknowledged, rather than a launch-day summary. Sponsored campaigns should expect limitations to be stated — a trade a governance vendor may want to make.

Examples: Source 1 · Source 2 · Source 3 · Source 4

It also explains where SecureAI does not apply.

• The post discloses vendor access but states no payment, sponsorship, or affiliate terms.
• It notes SecureAI was released October 6 and links to a 45-day trial.

Original disclosure

I received an early briefing on SecureAI and analyzed its core capabilities and current limitations.

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Bellamkonda says they first heard about the work from Cisco's Amin Karbasi at Black Hat in August. They treat the reported 0.823 score as a vendor claim until someone outside Cisco repeats it on their own code.

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One relays the Gemini agent announced October 8 at Gemini at Work 2026, including the per-project spending cap and missing unit price, and cites Bloomberg Media reporting of a 63% accuracy gain from the keynote. The other credits Mandiant, part of Google Cloud, for an incident described at the Cyber Defense Summit in which an agent ran up roughly $50,000 in cloud charges in under an hour.

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Bellamkonda says the announcement lowers setup barriers for IT teams but that the price is still high. They list three questions to put to Microsoft about fleet consoles, sandbox policy, and model versioning.

View example

In the Cycode posts, Slavin cites the Verizon DBIR finding that exploitation has passed stolen credentials as the top initial access route, along with the shift of discovery work to AI models.

• Slavin argues the bottleneck has moved to remediation: triaging, confirming and closing risk at the speed it is now found.
• Bellamkonda makes a parallel argument about agents themselves: approval is not oversight.
• Both turn a research or vendor claim into a statement about what an operating team must be able to do.

Slavin writes from inside a security vendor, pointing readers to its resource hub and category position.

• Bellamkonda writes as an outside reader of announcements, asking who outside the vendor has repeated a benchmark on their own code.
• Slavin focuses on AppSec and developer remediation at scale; Bellamkonda focuses on access review, named owners, spend ceilings and fleet management consoles for enterprise IT.

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Both writers address a business-technology audience about what an organisation needs in place before agents are useful and trusted, not just what the tools can do.

Eve stays at the strategy and leadership level, often anchored to a vendor event or partner programme and quoting other leaders' framings.

• Bellamkonda names specific products, prices, platform limits and test scores, then closes with questions readers can put to a vendor in writing.
• Eve describes the operating-model conversation; Bellamkonda tests the individual claims it rests on.

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Robbins's posts on India, Indonesia and Europe describe AI centres of excellence, sovereign security operations, research agreements and training goals—the same terrain Bellamkonda covers from the buyer's side.

The overlap is subject matter rather than method.

• Robbins writes as a company leader about visits, partnerships and announcements, centring the organisations and commitments.
• Bellamkonda writes as an outside analyst about what readers should ask before adopting a product, including limits left out of the announcement.

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The two posts draw on vendor research—IBM Think 2026 themes of trusted data, governance, hybrid cloud, and human leadership, and a Microsoft eBook on the CFO's role in cybersecurity—and turn it into a business-strategy case for including cyber risk and data governance in financial planning.

• For a webinar aimed at finance leaders, a CFO-facing article that quantifies cyber risk, or an event-anchored voice for an IBM or Microsoft partner track, Eve is a closer match than Shashi Bellamkonda's operational posts.
• The tradeoff: the posts offer strategic framing and partner participation, not methods for agent inventory, access review schedules, or spend ceilings.
• A vendor selling agent access and spend controls would need to supply the operational detail and use Eve to carry the governance-as-investment message.
• The IBM and Microsoft partner context also raises a fit question for a governance vendor whose competitive positioning overlaps with those ecosystems.

The posts focus on strategy and partnerships. They offer no pre-production checklist, spend-ceiling framework, or close analysis of an agent-governance product that could support the article series without the vendor supplying the operational content.

Examples: Source 1 · Source 2

The two posts show a speaker operating at that level: meetings with government leaders in India and Indonesia; an AI Center of Excellence with IOH, NVIDIA, and the Indonesian ministry; a sovereign security operations center; and a training commitment through Cisco Networking Academy.

• They also cover a European tour through MWC Barcelona, Zurich, Bern, and Rome on AI and cybersecurity.
• A vendor seeking executive authority, a government-and-partner audience, or a flagship event opening can use that platform.
• The tradeoff: the posts cover corporate travel, partnership announcements, and event recaps, not a buyer's method.
• The vendor would need to supply any pre-production checklist, cost-control framework, or close analysis of a governance product for the article series.
• Robbins's agenda is Cisco's, so the vendor would be renting association rather than teaching, and would need to build the governance message around the executive appearance.

The posts feature executive travel and partnership announcements, not a repeatable buyer method. A governance vendor would need to supply the operational guidance and use the appearance for credibility—a different value from a teaching series.

Examples: Source 1 · Source 2

The two posts show a founder-operator voice grounded in current data: the 2026 Verizon DBIR finding that vulnerability exploitation passed stolen credentials as the top initial access vector, a 34% year-over-year rise in median time-to-patch, supply chain involvement in 48% of breaches, and Forrester's first Agentic Development Security Landscape.

• Slavin then argues that Anthropic's Mythos and parallel discovery pipelines have commoditized finding flaws, shifting the constraint to triage, confirmation, and closure.
• A security-tooling vendor seeking a category-building webinar, a remediation-pipeline article, or a speaker for practitioner audiences can use that perspective.
• The tradeoff: the posts are closely tied to Cycode's product and category—the Forrester Landscape mention, the Mythos resource hub, and the 'Cycode is building against each' close.
• The vendor would be buying a competitor-adjacent founder's frame and need to distinguish its agent-governance message from Cycode's remediation pitch.
• The posts focus more on vulnerability discovery and remediation than on agent ownership, access review, or spend ceilings; ask the vendor to supply the specific operational guidance its buyers need.

The posts are tied to Cycode's product and category-building. A governance vendor would be buying a competitor-adjacent frame and would need to distinguish its agent-access message from Cycode's remediation pitch.

Examples: Source 1 · Source 2