Anuj Goel writes about cybersecurity, threat intelligence, and using AI to help security teams turn threat information into practical defensive action.
Anuj Goel is most useful for explaining how security teams can operationalize AI and threat intelligence into defensive action.
Best for
Cybersecurity vendors selling to enterprise security leaders needing thought leadership on operationalizing agentic AI in threat intelligence.
Not for
Product-led demand generation, short-form product walkthroughs, guaranteed impressions, or fixed cost per lead.
Key takeaways
The bottleneck is action, not threat data.
The real problem is the queue and the fatigue it creates.
Threat intelligence needs the customer's asset landscape to become actionable.
Check first: Confirm follower roles, employer, seniority, recent reach, engagement, and evidence of long-form or on-camera work.
Main ideas
The bottleneck is action, not threat data · The real problem is the queue and the fatigue it creates · Threat intelligence needs the customer's asset landscape to become actionable
Anuj Goel writes about cybersecurity, threat intelligence, and applying AI to help security teams act on relevant threats. Their central idea is that more threat data is not enough: teams need to understand which risks matter to their own assets and move from intelligence to timely defensive action.
Goel makes the shift from AI experimentation to operational use concrete.
In a Black Hat USA post on AI in threat intelligence, they ask whether agents can assess vulnerabilities in an organization’s context, prioritize what matters, and help teams act within trusted workflows.
A post about the Cyware–Armis partnership applies that idea to connecting threat intelligence with asset information, helping defenders identify which exposed systems warrant attention.
This practical focus is useful to readers evaluating AI for security operations, rather than AI as an end in itself.
Anuj Goel's posts make one argument: cybersecurity's hard problem is no longer collecting threat data but shortening the distance between a threat appearing and a defense being in place.
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Goel judges AI tools by whether they understand an organization's environment, stay within guardrails a team trusts, and fit workflows defenders already run—not by how much they produce. The same logic underpins a partnership announcement: visibility and threat intelligence become actionable only when mapped onto a customer's own assets.
The bottleneck is action, not threat data
Anuj Goel argues that the industry has moved from experimenting with AI in threat intelligence to operationalizing it, and that the buyer question has shifted from whether AI belongs in security to which agents deliver results.
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A useful agent understands a customer's environment, operates within boundaries a team can trust, integrates into existing workflows, and helps defenders decide faster.
Output volume is not the measure: generating more content does not shorten response time; understanding context does.
As offensive capabilities get cheaper and faster, Goel argues, whoever shortens the gap between a threat appearing and a defense being in place wins.
The Black Hat USA 2026 post lists four things security leaders want the tools to do: contextualize a threat against an organization's own assets, environment, and priorities; judge whether a newly disclosed vulnerability presents meaningful risk; prioritize what matters instead of deepening analyst fatigue; and carry intelligence the "last mile" from something an analyst knows to something the organization has acted on.
At the conference, Anuj Goel reports hearing these four capabilities discussed consistently, presenting them as buyer sentiment gathered in conversation rather than findings from their own research. The post closes by asking other attendees whether they are seeing the same shift.
The real problem is the queue and the fatigue it creates
AI is meant to address specific operational problems, not make alert volume seem unavoidable: newly disclosed vulnerabilities add to an already overwhelming queue, intelligence can be technically relevant without posing meaningful risk, and analyst fatigue builds when everything gets equal priority.
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In this framing, AI's value is subtracting from the queue, not adding to it. Prioritization matters most because that is where the damage to a security team shows up.
Anuj Goel describes a shift away from generic copilots and "AI for the sake of AI" toward agents that tell a team whether a newly disclosed vulnerability is a real risk and rank what deserves attention first, rather than handing over more alerts to triage.
Threat intelligence needs the customer's asset landscape to become actionable
In the partnership announcement, Anuj Goel sets out a three-part chain: visibility tells you what you have, threat intelligence tells you what to worry about, and neither alone tells you what to do next.
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The proposed fix is enrichment rather than aggregation: external threat feeds filtered by a customer's industry, tech stack, asset criticality, and exposure, then mapped onto that customer's asset landscape.
When a relevant threat is identified, the exposed high-value assets are already known, turning a feed into a defensive action.
Goel describes the intended result as a move from reactive firefighting to proactive, automated defense across the attack surface, with customer-specific context feeding agentic AI workflows.
The announced Cyware and Armis Centrix partnership pairs continuous real-time asset intelligence with threat feeds enriched per customer. Goel writes about it in the first person as work the two teams built together and points readers to the announcement page.
The post is promotional and describes intended benefits, not measured outcomes. It is where Goel's analytical argument and commercial position meet: the asset-centric framing from the Black Hat post reappears as a product claim.
For a clear picture of how the author sees the current state of threat intelligence, start here.
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It lays out the problem they keep returning to and the standard they apply to AI tools.
Written during Black Hat USA 2026, the post centers on a distinction the author says came up repeatedly in conversation: the question is no longer whether AI belongs in threat intelligence, but which agents deliver results.
It lists four things useful tools do: contextualize a threat against an organization's assets and priorities; assess whether a newly disclosed vulnerability poses a real risk; prioritize threats instead of adding queue noise; and carry intelligence through the “last mile,” from analyst awareness to organizational action.
The closing argument is that cybersecurity's bottleneck is not access to threat data but converting volume into timely decisions.
Agents earn their place by understanding a customer's environment, staying within guardrails a team trusts, and fitting existing workflows rather than producing the most output.
The post ends by asking other attendees whether they see the same shift.
For brands planning a campaign
A strong starting point for thought leadership aimed at enterprise security leaders: the author argues for operational value over novelty, giving a vendor a way to frame agentic AI around decision speed, guardrails and workflow fit rather than model capability.
This shows the commercial thesis behind the other posts: what pairing asset visibility with threat intelligence is meant to produce, and who the author builds it with.
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The author announces a partnership between Cyware and Armis, describing it in the first person as something the two teams built together.
The argument follows a chain: visibility shows what you have, threat intelligence shows what to worry about, and neither alone says what to do next.
The partnership maps external threat intelligence onto a customer's asset landscape, enriching feeds by industry, tech stack, asset criticality and exposure.
When a relevant threat appears, the exposed high-value assets are already identified, and that context feeds “Agentic AI workflows.”
The post names the intended outcome: a move from reactive firefighting to proactive, automated defense across the attack surface, and links an announcement page.
For brands planning a campaign
The author speaks concretely about joint vendor work and a specific customer problem—closing the gap between knowing and acting—making this a better fit for co-marketing or a partner announcement than a purely conceptual piece.
Turning threat intelligence into operational decisions
The clearest recurring argument across the posts is that cybersecurity's bottleneck is no longer access to threat data but the distance between a threat appearing and a defense being in place.
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In the Black Hat USA 2026 post, Anuj Goel describes the industry shifting from experimenting with AI in threat intelligence to operationalizing it, and frames the buyer question as which AI agents actually deliver results rather than which copilots exist. In this framing, practical value comes from understanding a customer's environment, operating within boundaries a team can trust, and fitting into workflows defenders already run.
Contrasts volume with context: generating more content does not shorten response time, but contextualizing a threat against an organization's own assets and priorities does.
For brands planning a campaign
A thought-leadership collaboration on operationalizing agentic AI fits a campaign focused on the same subject. The strongest angle is a piece that moves past generic AI-in-security claims and works through what a specific agent needs to be trustworthy and useful inside an existing security operation.
Prioritization and analyst fatigue as the real problem
A second thread names the operational symptoms AI is supposed to address: vulnerability disclosures that add another line to an already overwhelming queue, alerts that are technically relevant but not meaningfully risky, and analyst fatigue that builds when everything is prioritized equally.
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Anuj Goel lists four capabilities buyers now look for — contextualizing a threat against the organization's own environment, judging whether a disclosed vulnerability presents real risk, prioritizing what matters most, and carrying intelligence the last mile from something an analyst knows to something the organization has acted on.
For brands planning a campaign
This is the most concrete hook for a vendor audience. A collaboration could take one of the four capabilities and show, through a worked scenario, how prioritization changes a security team's queue rather than adding to it.
Asset-centric context as the bridge between visibility and action
The partnership post sets out a specific thesis: visibility tells you what you have, threat intelligence tells you what to worry about, and neither alone tells you what to do next.
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The proposed fix is to map external threat intelligence onto an organization's asset landscape — industry, tech stack, asset criticality, exposure — so that when a relevant threat is identified, exposed high-value assets are already known. The stated goal is a shift from reactive firefighting to proactive, automated defense, with that customer-specific context feeding agentic AI workflows.
Builds the argument around a three-part distinction — visibility, threat intelligence, and the action layer — and positions the integration as supplying the missing third part.
For brands planning a campaign
Useful for a vendor brief because it shows how Anuj Goel frames integrations: the value is in joining two data sets to answer a decision question, not in announcing a logo. A collaboration can follow that structure, starting from a buyer decision and showing which combined inputs produce it.
The shortest post is a Black Hat attendance note inviting connections and messages during the week.
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It shows a lower-stakes format alongside the analytical posts: event presence, peer conversation, and open invitations to meet rather than a structured argument.
Unlike the other two posts, this one carries no cybersecurity thesis; it shows event participation and conversational availability, not a subject position.
For brands planning a campaign
Event-timed collaborations are plausible.
A co-authored or sponsored piece built around a conference week would match an existing habit of using the event as a conversation hook.
Ask for the analytical substance the collaboration would build on.
Anuj Goel's posts mention one commercial partnership: Cyware's partnership with Armis, with ServiceNow mentioned in the same post.
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They also mention attendance at Black Hat USA 2026. Ask for their role at Cyware and confirmation of any direct partnership with Armis or ServiceNow, or sponsorship of Black Hat USA 2026, if those details affect a potential collaboration.
Cyware
Disclosed partnership
Anuj Goel announced Cyware's partnership with Armis to deliver asset-centric threat context and said they were proud of what both teams built.
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Ask for their role at Cyware if it affects how you assess their authority to speak for the company.
Anuj Goel's posts are worth following if you're interested in what happens after threat intelligence arrives: the queue, the prioritization, and the system an agent has to fit into.
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Start with the two Black Hat USA 2026 posts, which argue that the bottleneck in cybersecurity is action rather than data and list the four things buyers now expect AI agents to do.
The partnership announcement shows the same idea applied to a specific product integration.
Read together, the posts show how Goel frames vendor claims: value comes from mapping threats onto a customer's own assets, not from producing more output.
From there, three creators take the same broad subject in different directions.
Deepen Desai writes from inside a vendor platform launch and frontier-model testing program; Jim Manico argues about the economics of application security tooling and answers skeptics directly; John Jeremiah pairs conference presence with survey research on security automation.
All three offer useful perspectives on the AI-in-security debate beyond threat-intelligence operationalization.
Both write about operationalizing AI inside security operations rather than treating it as an experiment, and judge tools by whether they shorten the time between detecting a threat and containing it.
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Deepen Desai's launch post makes the same case as Anuj Goel's posts about queue pressure and fragmented tools: security operations centers were not built for attackers who automate and chain exploits, and the fix is fewer manual steps and closed-loop response. Desai's post on testing frontier models against real attack and defense scenarios also matches Goel's interest in what agents can actually do: multi-step reasoning that constructs exploit paths rather than only scanning for known vulnerabilities.
Deepen Desai writes from inside a specific platform launch and an early-access model testing program, naming partner organizations and product capabilities. Anuj Goel reports buyer sentiment from conference conversations and argues about how integrations should be framed, with less product detail and no named test program.
Both argue that AI is changing the speed and economics of security work, and take a position against skepticism rather than hedge.
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Jim Manico's post on collapsing cybersecurity business models makes a structural claim close to Anuj Goel's: what matters is no longer how many vulnerabilities you find but how quickly exploitable risk is eliminated, with independent verification and continuous testing as the safeguard. Manico's reply to the "AI cyber is BS" argument, citing an active attack in progress, makes the same kind of case from current evidence that Goel makes after Black Hat.
Jim Manico focuses on application security and the tooling market—scanners, SAST, DAST, penetration testing—and argues about AI-native software engineering and standards such as OWASP ASVS. Anuj Goel focuses on threat intelligence and the last mile between an analyst knowing something and an organization acting on it, rather than AppSec tooling economics.
Both use Black Hat as a regular venue and address security leadership audiences on where automation and AI are heading.
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John Jeremiah's summary of a survey of more than 1,000 security decision-makers covers the same territory Anuj Goel describes from the conference floor: automation as a strategic priority, the rise of agentic AI, long deployment times, and the small share of teams with mature programs.
John Jeremiah's posts promote events and summarize survey findings with percentages, while Anuj Goel makes first-person analytical arguments about prioritization and integration theses, using numbers sparingly and offering no survey of their own.
Anuj Goel's posts cover cybersecurity, threat intelligence, and moving AI agents from experiment to daily defensive operations—the ground a proposed collaboration would cover.
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Their LinkedIn following stood at 7,268 accounts on 8 October 2026, but the posts do not identify followers' roles, employers, seniority, or the reach and response individual posts receive.
For a vendor selling to enterprise security leaders, the key question is whether those people are among the readers.
Treat the follower count as a ceiling on possible distribution, not proof of it.
Before committing budget, request recent impressions, comments, and shares for posts on comparable topics and in comparable formats, plus any follower company, industry, or job-title data they can share.
If an audience survey exists, ask for its aggregate results: a standard platform demographics export covers geography, age, and gender, not employer, occupation, or seniority.
LinkedIn followers
7,268
LinkedIn profile follower count for Anuj Goel · 2026-10-08
Anuj Goel's posts do not describe creator partnership or sponsorship formats.
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The only partnership they describe is a company-level one: Cyware, identified in the posts as their company, announced a tie-up with Armis to add asset context to threat intelligence and route it into agentic AI workflows.
That vendor-to-vendor integration describes how two products are combined and sold together, not the terms under which a brand could commission content.
The posts provide no sponsor policy, rate card, media kit, disclosure practice, or partnership inquiry route.
A sponsor or agency interested in working with Goel should ask directly for contact terms.
A cybersecurity vendor selling to enterprise security leaders commissions a thought-leadership collaboration on operationalizing agentic AI in threat intelligence: a co-authored article and a recorded conversation arguing that the useful question is no longer whether AI belongs in threat intelligence, but which agents change a defender's decisions.
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Reasons
The core thesis is already on record. In the Black Hat USA 2026 post, Anuj Goel writes that the industry is moving from experimenting with AI to operationalizing it, and that buyers are asking which agents deliver results instead of which copilots exist. A vendor can develop that argument rather than supply it.
The post names the standard a vendor's product would be judged against: an agent has to contextualize a threat against the organization's own assets, environment and priorities; judge whether a newly disclosed vulnerability is a real risk; prioritize instead of adding queue noise; and carry intelligence the last mile from analyst knowledge to organizational action. That gives a collaboration concrete, testable claims.
The closing argument in the same post — that agents earn their place by understanding a customer's environment, staying inside guardrails a team trusts, and fitting workflows defenders already run — uses the language of enterprise security leaders. The piece can open with the answer rather than a product pitch.
Anuj Goel demonstrates commercial framing in the first person. The Cyware and Armis post announces a joint capability, states the problem in three parts (visibility tells you what you have, threat intelligence tells you what to worry about, neither alone tells you what to do next), describes the combined inputs, and names the intended outcome. That is the structure a co-marketing or partner piece needs.
There is a timing hook. The analytical post is written from Black Hat USA 2026 and ends by asking other attendees whether they see the same shift, giving a conference-week launch or follow-up discussion a natural occasion.
Caveat
The audience case rests on topic match and writing, not demonstrated access to enterprise security leaders: the only figure available is a LinkedIn following of 7,268 accounts recorded 8 October 2026, with no company, seniority, or per-post reach and engagement data. No sponsorship format, rate, disclosure practice, or inquiry route appears in the posts; agree terms directly. Only three short LinkedIn posts were available, so ask for evidence of long-form and on-camera work before choosing those formats.
Verdict
Strong fit on substance. Anuj Goel already argues this position in public, so the collaboration would build on a stated view rather than ask a contributor to adopt the vendor's.
When to choose a specialist
An endpoint security vendor wants a product-led demand-generation campaign: short-form video walkthroughs of its own console, gated how-to content, and a monthly commitment of posts with guaranteed impressions and a fixed cost per qualified lead, targeted at a specified mix of security job titles.
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Reasons
Nothing in the posts is a product demonstration, tutorial, or walkthrough. Two are industry commentary and one is a company partnership announcement. They show an executive-voice commentator, not a creator producing hands-on product content.
The posts offer no creator-collaboration format to price, sponsor policy, rate card, media kit, or partnership inquiry route. The Cyware and Armis partnership is a vendor-to-vendor integration that combines two products, not a brand commission for content, so it provides no basis for a paid performance engagement.
Impression and lead-volume guarantees cannot be underwritten. The audience section found only a follower count of 7,268 recorded 8 October 2026 and no job-title, employer, or seniority data; a brief promising a defined mix of security leaders has no evidence to support it.
The posts cover a narrow subject range: agentic AI in threat intelligence, asset-centric context, and security operations prioritization. Tool-level tutorials for a different product category would rely on capability the posts do not demonstrate.
The format itself is the mismatch. The strongest demonstrated work is argument and framing — contrasting volume with context, distinguishing visibility from action. Turning that into a console demo would discard the strength the posts support.
Caveat
This is a fit judgment on the material reviewed, not a statement that Anuj Goel would refuse such work or has never done it. A performance brief could become viable if the person can supply evidence of prior product-led content, reach and engagement figures for comparable posts, and follower job-title or company data.
Verdict
Poor fit based on the posts. A campaign needing promotional, demonstration, and measurable-distribution capabilities would rely on capabilities the posts do not show.
A worked campaign example
Angle
“The last mile: what has to be true before an agent can act on threat intelligence.” Take Anuj Goel's own framing — useful agents contextualize a threat against the organization's assets and priorities, judge whether a vulnerability is a real risk, prioritize rather than add queue noise, and carry intelligence from analyst awareness to organizational action — and argue the buyer-side version: what a security leader should demand of an agent before letting it act, and where the handoff from knowing to acting breaks.
Beats
Open with the shifted question from the Black Hat USA 2026 post: not whether AI belongs in threat intelligence, but which agents deliver results.
Walk one scenario end to end — a newly disclosed vulnerability that is technically relevant to many organizations but not a real risk to a specific asset set — and show where asset, industry, and exposure context changes the decision and the queue.
Name the guardrail question in the contributor's own terms: understanding the customer's environment, operating inside boundaries a team can trust, and fitting workflows defenders already run.
Give readers a last-mile test: did the intelligence change what the organization did, and can the team show that it shortened the distance between a threat appearing and a defense being in place?
Close with a short checklist of questions to ask any agent vendor, giving readers a usable decision aid rather than a product claim.
Before committing
Confirm willingness to author under a sponsor relationship and how disclosure would be handled. The posts give no sponsored-content format, disclosure practice, or inquiry route, so establish these before making an offer.
Ask for reach and engagement on comparable posts — impressions, comments, shares — and any follower company, industry, or job-title data. The audience review found only a follower count of 7,268 recorded 8 October 2026, leaving the case that enterprise security leaders are reading untested.
Ask for evidence of long-form and on-camera work before locking the article-plus-recording format; only three short LinkedIn posts were available for review.
Confirm whether a conference-week launch is realistic within the campaign window; the Black Hat hook comes from the contributor's event post, not a confirmed speaking or attendance plan.
Put the commercial terms and review rights for the vendor's legal and product claims in writing; ask for them, as none are documented in the material reviewed.
Format
One co-authored long-form article of roughly 1,800–2,500 words on the vendor's site, plus a short authored companion post by Anuj Goel linking to it; a 30-minute recorded conversation with the vendor's product lead working through two or three concrete decision scenarios; an optional conference-week meetup used as a reporting hook rather than a sales event.
Why it fits
Every beat draws on a position Anuj Goel has already stated publicly: the volume-versus-context argument and the four capabilities in the Black Hat post, and the three-part visibility, threat-intelligence, action distinction in the Cyware and Armis post. They would extend a publicly stated argument rather than endorse a vendor's, making the thought-leadership collaboration credible. It also uses the one commercial format the posts show — first-person framing of joint value with a named partner — rather than inventing a creator format.
Choose John Jeremiah when the collaboration's spine is market evidence and event activation rather than a first-person thesis about threat-intelligence operations.
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The July 2025 post summarizes a BlinkOps survey of more than 1,000 security decision-makers: 81% call automation a top strategic priority, 53% plan to use AI agents for autonomous threat detection, and only 6% report mature automation programs.
That gives a vendor concrete data points to anchor an article and a conference conversation.
The August 2023 Traceable post shows a working event format: booth demos, expert talks, and a clear API security, Zero Trust, and fraud audience.
This is a better match when the vendor needs a data-led narrative, a conference-week hook, and someone comfortable promoting a joint presence.
The posts offer less on threat-intelligence operationalization.
They do not define what an agent should do with a threat feed, how prioritization changes an analyst's queue, or where intelligence moves from awareness to action.
The 2025 post amplifies a report; the 2023 post promotes an event.
A vendor would get adoption statistics and event reach, but not the close argument about agent context, guardrails, and last-mile action in Anuj Goel's posts.
Ask for sponsorship terms, reach, and audience composition.
Choose Deepen Desai when a vendor needs someone who can speak from the product and testing side of agentic security operations.
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The September 2026 Zscaler Agentic Security Operations post describes attackers automating attack stages, chaining exploits, and compressing defender response time, then lists product capabilities: unified exposure and threat management, inline telemetry across network, identity, endpoint, cloud, AI and deception, specialized AI agents shaped by frontline SOC, MDR and threat-hunting experience, and closed-loop remediation.
The May 2026 Project Glasswing post adds specific frontier-model observations: multi-step reasoning constructing exploit paths, architectural context improving exploit accuracy, and context used only to ground the model.
For a vendor seeking a technical authority on agentic SecOps, attacker speed, and closed-loop remediation, this is a stronger starting point than Anuj Goel's high-level 'which agents deliver results' framing.
The posts focus on launches and partnerships.
They explain what a vendor has built, but are less suited to a neutral buyer's guide telling security leaders what to demand before letting an agent act.
A collaboration with Deepen Desai could read as product news, and the argument may need to focus on one vendor's platform rather than the threat-intelligence market.
Ask how Deepen Desai handles independent thought leadership, sponsored disclosure, and audience reach.
Choose Jim Manico when the campaign is about AI changing the economics of finding and fixing software vulnerabilities, not about operationalizing threat intelligence.
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The October 2026 post argues that AI agents can analyze codebases, discover vulnerabilities, develop exploits, generate patches, write regression tests, and verify results, and predicts that standalone scanners, SAST, DAST, IAST, and penetration testing will be replaced by AI-native capabilities.
The September 2026 post points to an active RubyGems agent-swarm attack and argues that AI already speeds recon, code analysis, vulnerability research, and exploit development.
For an AppSec, secure-SDLC, or code-security vendor, this is a direct fit, with a sharper contrarian argument than Anuj Goel's threat-intelligence framing.
The tradeoff is scope.
These posts focus on vulnerability discovery, remediation economics, and application security standards such as OWASP ASVS and AISVS.
They do not address threat-intelligence prioritization, asset-centric context, analyst queue fatigue, or the last mile from knowing to acting—the focus of a campaign on threat-intelligence prioritization.
A vendor would get a strong AI-and-AppSec argument, but would need to reframe its product away from threat intelligence.
External links and claims would also need verification before a co-authored article.
Ask for sponsorship terms and audience composition.
Three LinkedIn posts from the account linked here cover Black Hat USA 2026: two posts on operationalizing AI in threat intelligence and on adding asset context to threat data, and a short note about attending the conference.
The posts have no visible publication dates, and they cover the late July to early August 2026 conference period.
No talks, long-form articles, transcripts, or interviews appear in the posts here, so they reflect the arguments in those posts rather than a complete picture of Anuj Goel's work; that gap remains open.
Similar-creator comparisons draw on two posts each from Deepen Desai, Jim Manico, and John Jeremiah.
The posts here do not state Anuj Goel's employer or role, and this page does not claim one.
