# Anuj Goel

Anuj Goel writes about cybersecurity, threat intelligence, and using AI to help security teams turn threat information into practical defensive action.

- Profile: https://b2binfluencer.marketing/creators/anuj-goel/
- LinkedIn: https://www.linkedin.com/in/goelanuj
- LinkedIn followers: 7,268
- Topics: artificial-intelligence, cybersecurity, agentic-ai, threat-intelligence
- Based on 9 dated posts, latest 2026-10-10

## Summary

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.
- Check first: Confirm follower roles, employer, seniority, recent reach, engagement, and evidence of long-form or on-camera work.

### 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.

## Main ideas

- **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. 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 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. 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.
- **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. 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.

## Work to read

- [On the shift from experimenting with AI to operationalizing it (Black Hat USA 2026 field notes)](https://www.linkedin.com/posts/goelanuj_ai-blackhat-activity-7490767794691674112-B2qH)
- [Announcing asset-centric threat context with Armis Centrix](https://www.linkedin.com/posts/goelanuj_cybersecurity-threatintelligence-ai-activity-7488212924126281728-7ajv)

## Brand relationships

- **Cyware** (disclosed_partnership)
- **Armis** (mention)
- **ServiceNow** (mention)
- **Black Hat** (mention)

## Partnerships

Anuj Goel's posts do not describe creator partnership or sponsorship formats. 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.

## Alternatives

- John Jeremiah: Choose John Jeremiah when the collaboration's spine is market evidence and event activation rather than a first-person thesis about threat-intelligence operations. 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.
- Deepen Desai: Choose Deepen Desai when a vendor needs someone who can speak from the product and testing side of agentic security operations. 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.
- Jim Manico: Choose Jim Manico when the campaign is about AI changing the economics of finding and fixing software vulnerabilities, not about operationalizing threat intelligence. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/