Nancy Wang is a technology investor focused on cybersecurity and AI. Wang has led investment in security companies and founded Advancing Women in Tech, a nonprofit that offers practical career education.
Nancy Wang is most useful for practitioner conversations and advice on AI agent security and secure-by-design.
Best for
A cybersecurity vendor launching AI agent security work needing a moderator, interviewer, or advisor.
Not for
A consumer mobile app or lifestyle brand seeking high-volume paid social installs.
Key takeaways
Put security inside the AI generation loop, not bolt it on afterward.
Treat every AI agent as untrusted and control what it can reach.
AWIT partnered with Anthropic and Coursera for practical AI education.
Check first: Confirm current availability, vendor-sponsored work, conflicts or exclusivity, and audience composition.
Main ideas
Put security inside the AI generation loop, not bolt it on afterward · Treat every AI agent as untrusted and control what it can reach · Protect AI at runtime against concrete, named attacks
Nancy Wang is a technology investor focused on cybersecurity and AI, and the founder of Advancing Women in Tech (AWIT), which provides practical career education.
Their posts feature conversations with security practitioners and explain emerging AI security issues.
A recurring concern is how to build safeguards into AI systems: treat agents as untrusted, limit and monitor their access, and put security into AI-generated code rather than relying only on later review.
They also highlight practical AI education, including AWIT’s course partnership with Anthropic and Coursera.
Wang connects technical security questions to concrete decisions and people.
In a post about AI coding agents, they explain how ambiguous instructions and competing priorities can leave security behind, then describe Corridor’s approach of putting guardrails into the coding process.
In a 1Password episode introduction, they turn agent security into practical principles: assume an agent could be compromised, limit its access, and monitor its actions.
Their Black Hat panel plans to bring practitioners into a discussion of agent identity, authority, exploits, and human control.
Their posts also show a different, education-focused strand: AWIT’s work is framed around making useful career and AI skills more accessible.
Nancy Wang's public posts return to one argument: build security into how AI systems are made and run, rather than adding it after something goes wrong.
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They apply that argument at three points—inside AI coding agents that write production code, at the boundary where an agent gets access, and in AI models' runtime defenses against live attacks—and pair it with a second priority: practical education as a path into technology careers. Guests mainly support Wang's points: Travis McPeak (Head of Security at Cursor) supplies the untrusted-agent framing, while the Black Hat post lists panelists without detailing their views.
Put security inside the AI generation loop, not bolt it on afterward
Wang argues that AI coding agents can produce insecure code even when the models understand security, because security is not the optimization target.
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Ambiguous specs and competing priorities let insecure code through, while loops that run faster than human review widen the gap between generation and review. Wang endorses embedding security constraints in the generation step: what the spec emphasizes shapes what you get, so security has to be part of the spec.
Announcing a Series A for Corridor, Wang describes 2026 '#Ralph loops' in which an agent gets a context window and a spec, builds, verifies, and repeats—generating entire features, refactoring codebases, and running migrations on its own. They point to Jack Cable's work leading the Cybersecurity and Infrastructure Security Agency's Secure by Design initiative and describe Corridor's approach: security guardrails inside AI coding via MCP, automated security review on every pull request, and organization-wide visibility for security teams.
The Operant AI post addresses threats to live models; the Corridor post moves the same concern earlier, into code generation, arguing that chasing individual bugs cannot fix vulnerability classes.
Treat every AI agent as untrusted and control what it can reach
Wang's preferred posture is to assume agents can be compromised.
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That leads to concrete design choices: limit what an agent can access, monitor what it does, and put a policy enforcement layer at the boundary. They frame secure-by-default as a business philosophy, not a compliance checkbox, and describe agent identity as an unresolved industry question that companies are working around rather than waiting for.
Introducing a 1Password Zero-Shot Learning episode, Wang summarizes a conversation with Travis McPeak, Head of Security at Cursor, who previously held security roles at Netflix and Databricks. They highlight McPeak's takeaway—that each agent should be treated as untrusted, especially around sensitive data—and list the episode's questions: why agent identity is still open, why securing an agent differs from securing a user, how to build policy enforcement at the boundary, and why practitioners who reason from first principles will expand their reach.
Protect AI at runtime against concrete, named attacks
Wang's argument about AI security as a market centers on speed and specificity: attackers are scaling faster than defenses, so defenses must evolve at the same rate.
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They name attack classes and when they occur, pointing to real-time detection and automated enforcement as the response.
Wang writes that nation-states and criminal syndicates are weaponizing AI to scale cyberattacks, cites Gartner's Market Guide on AI Trust, Risk, and Security Management (AI TRiSM), and describes Operant AI's runtime security: detecting threats in real time, enforcing automated protections, and preventing prompt injection, data exfiltration, and model theft before they compromise models.
Make overwhelming volumes of security data usable in real time
Wang treats security data volume as one of the field's hardest practical problems, not a tooling detail.
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They value AI-native data pipelines because they let enterprise security teams work with telemetry as it arrives instead of drowning in it.
Writing about Observo AI's move to SentinelOne, Wang calls making overwhelming volumes of security data usable in real time one of cybersecurity's hardest problems. They describe the team's AI-native data pipeline as category-defining and argue that combining it with SentinelOne's platform will bring new capabilities to enterprise security teams worldwide.
Convene practitioners and ask uncomfortable questions
Wang advances the debate by assembling practitioners and pressing them on unresolved problems rather than restating that AI is changing everything.
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Their questions focus on what remains open: how an agent gets identity and authority, what differs between open and closed models, what recent exploits exposed, and where human control still has to stay.
Announcing a Black Hat panel, Wang calls it 'basically a group chat with a stage' and names Jacob DePriest (CISO, 1Password), Drew Hintz (Head of AppSec, OpenAI), Jonathan Aluveaux (CIO, Legora), Kyle Polley (CISO, Perplexity), Tom Alcock (Founder, Code Red Partners), and Jake Storm (General Partner, Felicis) as people to ask for the unvarnished version. They say the session will not be another 'AI is changing everything' panel and invite uncomfortable questions.
Practical, workplace-ready education as a path into technology
Alongside the security arguments, Wang emphasizes access: concrete, job-relevant skills can open technology careers, and the effort centers learners rather than prestige.
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They tie this to their experience growing up in rural Wisconsin, crediting small moments of access—a teacher, a program, scholarships—with opening a path that had not seemed available.
Wang marks roughly 100,000 learners across Advancing Women in Tech courses in product management, engineering, cybersecurity, and AI.
They announce that Anthropic and Coursera partnered with the nonprofit on Real-World AI for Everyone, a three-course specialization in practical, workplace-ready AI skills, with the stated goal of helping anyone in any role use AI in their job.
Wang founded AWIT in 2016 as a 501(c)(3) focused on expanding access to career-focused education and names the instructors involved.
Wang's education thread predates the security material, beginning with the nonprofit's 2016 founding.
Shows how Nancy Wang identifies unresolved practitioner questions in AI agent security and convenes senior security leaders to address them.
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Wang hosts a Black Hat panel described as “basically a group chat with a stage.”
Panelists include Jacob DePriest (CISO, 1Password), Drew Hintz (Head of AppSec, OpenAI), Jonathan Aluveaux (CIO, Legora), Kyle Polley (CISO, Perplexity), Tom Alcock (Founder, Code Red Partners), and Jake Storm (General Partner, Felicis).
Wang says the session will not be another “AI is changing everything” panel.
Instead, it will address how an agent gets identity and authority, what changes between open and closed models, what recent exploits exposed, and where human control needs to stay in the loop.
The format is a live practitioner conversation built around uncomfortable questions.
For brands planning a campaign
For a cybersecurity vendor planning a speaker or advisor partnership on AI agent security, this is direct evidence that Wang can frame and moderate the debate—and that their network reaches CISOs and AppSec leaders at 1Password, OpenAI, Perplexity, and beyond. A vendor could propose them as a moderator, panel host, or advisory voice for an agent-security launch.
Shows how Nancy Wang explains the security posture businesses should take toward AI agents, using a named practitioner and a concrete framework.
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Wang introduces a 1Password Zero-Shot Learning episode with Travis McPeak, Head of Security at Cursor, who previously held security roles at Netflix and Databricks.
They highlight the takeaway of treating each agent as untrusted, especially when sensitive data is involved: assume agents could be compromised, limit access, monitor actions, and control risks.
The episode covers why agent identity remains an open question, why secure-by-default is a business philosophy rather than only a compliance requirement, why securing an agent differs from securing a user, how to create a policy enforcement layer at the boundary, and why security practitioners who reason from first principles will expand their reach.
For brands planning a campaign
This gives a vendor a ready-made content angle: Wang can speak to agent identity, policy enforcement at the boundary, and treating agents as untrusted. A speaker or advisory partnership could center on those themes rather than generic AI risk.
Shows what Nancy Wang looks for when investing in AI security and how they explain the gap between code generation and review.
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Wang announces that they are leading a Series A for Corridor with Jake Storm and Felicis, congratulates Jack Cable, Ashwin Ramaswami, and Alex Stamos, and credits Shreya Shekhar for an introduction.
They explain why engineers should care: in 2026, “#Ralph loops” allocate a context window, give the agent a spec, let it build and verify, then loop; they can generate entire features, refactor codebases, and run migrations autonomously.
Wang argues that models are not ignorant about security, but under ambiguous specs and competing priorities, “Security isn't the primary optimization target.”
Jack Cable led CISA's Secure by Design initiative, and Corridor puts security guardrails inside the AI coding workflow via MCP, automates security review on every PR, and gives security teams visibility across the org.
For brands planning a campaign
For an AI agent security vendor, Wang offers an investor’s thesis: secure-by-design must move into the generation step, not be bolted on afterward. They could advise or speak on secure-by-design, MCP guardrails, and automated PR review; the post also names founders and investors relevant to an agent-security campaign.
Shows how Nancy Wang frames runtime AI security and which threats they see as urgent.
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Wang writes that nation-states and criminal syndicates are weaponizing AI to scale cyberattacks faster than ever, and that security solutions must evolve just as fast. They cite Gartner's latest Market Guide on AI Trust, Risk, and Security Management (AI TRiSM) and introduce Operant AI, which tackles the problem by securing AI at runtime: detecting threats in real time, enforcing automated protections, and preventing attacks such as prompt injections, data exfiltration, and model theft before they compromise AI models.
For brands planning a campaign
This maps directly to an AI agent security brief. A vendor can position Wang as a speaker or advisor who frames runtime AI security through AI TRiSM, prompt injection, data exfiltration, and model theft—and connects those threats to a business audience.
Shows what kind of security data company Nancy Wang backed and how they describe a successful security acquisition or platform outcome.
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Wang says it has been a privilege to work with the Observo AI team from Day 1 and congratulates them on carrying the Observo journey forward at SentinelOne.
They describe Gurjeet A., Ricky S.
Arora, and the team as relentless in tackling one of cybersecurity's hardest problems: making overwhelming volumes of security data usable in real time.
Wang calls the AI-native data pipeline they built at Observo category-defining and says combining it with SentinelOne's platform will bring new capabilities to enterprise security teams worldwide.
The post tags Jake Storm, Felicis, Guru Chahal, Nnamdi Iregbulem, Lightspeed, and Alexa Fedyukova, and uses the hashtags AIObservability, Cybersecurity, and RubrikMafia.
For brands planning a campaign
This demonstrates Wang's security investing track record and operator network. They can speak credibly about AI-native security data pipelines and enterprise security adoption, and potentially make introductions across the Felicis and Lightspeed networks.
Shows what education work Nancy Wang is known for and how they connect practical AI skills to access.
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Wang says nearly 100,000 learners have taken AWIT's real-world upskilling courses in product management, engineering, cybersecurity, and now AI.
They announce that Anthropic and Coursera have partnered with Advancing Women in Tech to launch Real-World AI for Everyone, a three-course specialization focused on practical, workplace-ready AI skills.
Wang founded AWIT in 2016 as a 501(c)(3) nonprofit focused on expanding access to practical, career-focused education.
They reflect on growing up in rural Wisconsin, far from a tech career, and credit small moments of access: a teacher, a program, and scholarships.
Wang thanks Anthropic, Coursera, 1Password, and AWIT, and names instructors.
For brands planning a campaign
For a cybersecurity vendor, Wang can connect AI agent security to workforce education and access.
A partnership could include a co-hosted course module, a speaker session on practical AI and security skills, or an advisory role on education and talent.
The Coursera and Anthropic relationship demonstrates their experience working with major technology partners.
Wang’s posts approach agent security as an engineering and governance problem.
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In a post about a 1Password conversation with security leader Travis McPeak, they highlight treating each agent as untrusted, limiting its access, monitoring its actions, and enforcing policy at system boundaries. In a separate post about Corridor, they argue that security constraints should be built into AI coding workflows rather than added after code is generated.
They explain practical risks, name controls or design principles, and connect them to decisions security teams face.
For brands planning a campaign
A vendor focused on agent identity, access controls, monitoring, or secure coding could assess Wang for a practitioner discussion or advisory brief. Ask whether they are open to an endorsement or partnership; the posts do not establish either.
Wang describes AI security through concrete threats and real-time defenses.
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In a post about Operant AI, they name prompt injection, data exfiltration, and model theft, then describe runtime detection and automated protections as ways to address them.
For brands planning a campaign
This is relevant to a vendor seeking discussion of runtime AI protection. Ask whether Wang wants to examine the product category or specific technical tradeoffs; the post alone does not show independent product evaluation.
In a post about Observo AI joining SentinelOne, Wang frames the challenge as making large volumes of security data usable in real time.
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The example connects AI-native data pipelines with enterprise security operations.
For brands planning a campaign
A security-data or observability brief could draw on this operational framing, especially if it asks how teams turn telemetry into timely decisions. The post announces an investment-related development but does not compare products.
Practical education and access to technology careers
Wang presents workplace-ready education as a way to widen access to technology skills.
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In a post about an AI course specialization, they link practical learning to their account of founding Advancing Women in Tech and its career-focused courses.
For brands planning a campaign
This subject may suit a campaign about practical AI learning, workforce development, or access to technology careers. It is distinct from the security-focused material and is relevant when a campaign values education and access as well as technical expertise.
Wang’s posts connect security founders, investors, and practitioners.
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A Corridor announcement explains why they consider the team’s security background relevant to AI-generated code; a Black Hat panel announcement frames questions about agent authority, model openness, exploits, and human control.
The posts pair a specific company or event with a stated problem and questions for practitioners to discuss.
For brands planning a campaign
For a speaker or advisor brief on AI agent security, these posts offer direct evidence of interest in the topic and convening practitioner discussion. Ask about the scope of any advisory work and current availability.
Nancy Wang's posts show an investor who works with security and AI companies and founded an education nonprofit.
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They say they led Corridor's Series A with the Felicis team, worked with Observo AI from its early days through its acquisition by SentinelOne, founded Advancing Women in Tech in 2016, and partnered with Anthropic and Coursera on an AI course specialization.
They also promote 1Password's Zero-Shot Learning series, highlight Operant AI, and host a Black Hat panel on AI agent security.
Treat company spotlights as mentions unless sponsorship is confirmed.
Advancing Women in Tech
Owned business
Wang says they founded Advancing Women in Tech (AWIT) in 2016, a 501(c)(3) nonprofit focused on practical career education whose courses have reached more than 100,000 learners.
Wang says they led Corridor's Series A alongside Felicis general partner Jake Storm, backing founders Jack Cable, Ashwin Ramaswami, and Alex Stamos on security guardrails embedded in AI coding workflows.
Wang says they have worked with the Observo AI team “from Day 1” and congratulates the team on its acquisition by SentinelOne, crediting Felicis and other colleagues.
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The post describes the firm's early involvement in the AI security data pipeline company.
Wang promotes 1Password's Zero-Shot Learning series, including an episode with Cursor security head Travis McPeak, and thanks 1Password for joining the AWIT AI course launch.
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Ask Wang to clarify their formal role with the company.
Wang dedicated a post to Operant AI's runtime AI security product, crediting it with detecting threats and blocking prompt injection, data exfiltration, and model theft.
Wang hosted a Black Hat panel on AI agent identity and authority, open versus closed models, recent exploits, and human oversight, with security leaders from 1Password, OpenAI, Legora, Perplexity, Code Red Partners, and Felicis.
Nancy Wang's material offers two distinct entry points.
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The larger is AI security: they argue that security must sit inside the AI coding loop and at the boundary where an agent gets access, rather than being added after release.
The same instinct runs through their posts on runtime protection for live models and making security telemetry usable as it arrives.
A smaller thread covers practical, workplace-focused AI education and access to technology careers, tied to a nonprofit founded in 2016 and a three-course specialization.
For more on the security subjects, readers can follow practitioners publishing their own technical commentary and curation: Ronen Slavin writes about the remediation bottleneck created by faster AI-driven vulnerability discovery, Clint Gibler publishes a weekly security reading digest with an AI-and-security section, and Motti Sorani argues for remediation and the 'last mile' in application security.
Wang's posts comment on named companies, funding, and events, often ending with a question readers can take into a team discussion.
Both write about how AI's faster, cheaper flaw discovery changes security practice.
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Slavin's posts address the same problem Wang raises in the Corridor and agent-security material: the discipline must change its architecture, not just add tooling, and shift from detection toward remediation under time pressure.
Slavin argues from inside a security vendor building agentic remediation, using reports and category landscapes to make his case. Wang's posts focus on companies, investments, and events, bringing in guests and panelists rather than describing a product in development.
Gibler covers the same beat as Wang, with a recurring AI-and-security section featuring agent security, supply chain, and AppSec work.
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Readers seeking a steady stream on the subjects in their posts will find it here, including AI agent and vulnerability themes.
Gibler publishes a dated reading list of short headlines and links to others' work, with almost no argument of his own. Wang comments on named companies and events and invites practitioners into the discussion, offering analysis and conversation rather than curation.
Sorani's argument that security teams need fixes, not more alerts, matches Wang's emphasis on embedding security into the process and using data as it arrives.
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Both see remediation and operational follow-through—not discovery—as security's hard part.
Sorani writes as a cofounder of an application security company, drawing on conversations about offensive security origins and the 'last mile' of remediation. Wang ranges wider, moving between AI security arguments, an education nonprofit, and event hosting.
Nancy Wang's LinkedIn following is listed as 35,926 on 8 October 2026.
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Their posts cover secure-by-design engineering, AI agent identity and runtime security, security investing, and career education through Advancing Women in Tech, suggesting an audience of security and engineering practitioners and startup operators.
For a campaign aimed at security practitioners, treat Wang as reachable to roughly 36,000 LinkedIn followers, but request platform or survey data on followers' locations and roles, plus performance data for recent posts on comparable topics, depth, and formats (for example, agent security deep dives versus event announcements) to assess audience overlap with your buyer.
Six posts published between February 2025 and July 2026 do not show engagement counts.
LinkedIn followers
35,926
Nancy Wang LinkedIn profile, as recorded in the account record for this review · 2026-10-08
Nancy Wang operates in four partnership-shaped roles: hosting a practitioner panel at Black Hat, fronting a security interview series for 1Password, co-developing nonprofit AI courses through Advancing Women in Tech, and investing in early-stage security companies through Felicis.
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For a cybersecurity vendor weighing a speaker or advisor engagement on AI agent security, the closest precedents are the Black Hat panel, where they assembled CISOs, founders, and investors for unscripted practitioner discussion, and the recurring AI-agent-security theme in the investment and course work. Ask directly about pricing, turnaround, approval rights, deliverables, exclusivity, lead time, and any sponsorship policy or media kit.
Conference panel hosting and moderation
Buyer requirement
Wang's Black Hat post describes hosting a panel built as "a group chat with a stage," featuring named CISOs, a founder, and an investor.
The format invites uncomfortable practitioner questions rather than a rehearsed industry keynote.
A sponsoring vendor or one supplying a speaker should contribute a named practitioner with direct operational experience, accept unscripted discussion, and agree on a topic specific enough to work in that format—in this case, agent identity and authority, open versus closed models, and where human control stays in the loop.
The post lists the date, time, and RSVP link, so the buyer handles event-side arrangements.
Ask who funds and approves the panel.
Interview or podcast series appearance
Buyer requirement
The 1Password "Zero-Shot Learning" episode with Travis McPeak, promoted through Wang's account, uses a technical interview format, with four to five concrete takeaways in the promotional copy.
A vendor seeking a similar placement should supply a guest with specific, current operating experience; the example guest ran security at Netflix, Databricks, and Cursor.
The topic should offer first-principles content, such as treating each agent as untrusted.
Ask whether Wang hosted or produced the episode, how guests are selected, and what the sponsor receives before treating this as a purchasable format.
Nonprofit course partnership
Buyer requirement
Anthropic and Coursera launched the "Real-World AI for Everyone" specialization with Advancing Women in Tech, the nonprofit Wang founded in 2016.
This co-developed educational asset involved named instructors, a three-course structure covering Product Management, Engineering, Cybersecurity, and AI, and co-branding across all three organizations.
A vendor pursuing this route should plan for course design, instructor participation, and a curriculum cycle rather than a single campaign flight.
Wang states that the AWIT courses have reached over 100,000 learners.
Ask about cost, revenue share, and sponsorship terms.
Investment and advisor relationships
Buyer requirement
Wang's investment work appears through Felicis, including leading Corridor's Series A alongside Jake Storm and working with Observo AI "from Day 1" through its acquisition by SentinelOne.
These investment announcements show advisory-style involvement in early security companies, not sponsorship offers.
A vendor seeking an advisor on AI agent security should expect engagement tied to company-building decisions: secure-by-design principles, embedding security guardrails into AI coding workflows, and security data pipelines.
Ask whether advisory capacity is available and on what commercial terms.
A cybersecurity vendor launching an AI agent security product wants a speaker and advisor who can host a practitioner conversation and pressure-test its agent-security positioning.
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Nancy Wang is a strong fit.
Their Black Hat panel post shows they can convene CISOs, an AppSec leader, a security founder, and an investor for an unscripted discussion on the campaign’s core topics: how an agent gets identity and authority, what changes between open and closed models, what recent exploits exposed, and where human control stays in the loop.
The 1Password episode post offers a ready-made framework: treat each agent as untrusted, limit access, monitor actions, and enforce policy at the boundary.
The Corridor post argues that security belongs inside the AI coding loop, not bolted on afterward.
The Operant AI post gives the vendor runtime security language to use with buyers: prompt injection, data exfiltration, model theft, real-time detection, and automated protection.
Investment posts with Corridor and Observo AI show their close work with early security companies and their perspective on secure-by-design, MCP guardrails, automated PR review, and security data pipelines.
The posts support hiring them as a moderator, technical interviewer, or advisor rather than for a conventional paid post.
Before contracting, ask about current availability, vendor-sponsored work, conflicts or exclusivity, and audience composition.
When to choose a specialist
A consumer mobile app or lifestyle brand seeking a high-volume paid social campaign to drive mass-market installs through follower reach and conversion content is a poor fit for Nancy Wang.
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Their LinkedIn posts focus on AI agent security, runtime security, cybersecurity investing, and workforce education through Advancing Women in Tech.
They show no consumer lifestyle content, product-review formats, app-install promotions, or conversion creative.
The only audience figure provided is a LinkedIn following of 35,926, with no breakdown by geography, age, occupation, or seniority and no engagement counts.
The topic mismatch is the larger problem: a consumer brand would be asking them to reach an audience whose interests and roles are unverified and whose platform behavior appears professional rather than consumer-focused.
A security software vendor can use their subject matter, network, and practitioner formats.
The posts do not support a paid reach or conversion recommendation for a consumer app.
A worked campaign example
Campaign angle: “Who is your agent?” — a moderated practitioner panel plus a short advisory engagement for a security vendor selling agent identity, access control, or runtime protection.
Step 1: Propose a 45–60 minute live or virtual panel hosted by Nancy Wang, structured as an unscripted group chat with a stage rather than a slide-led keynote, using the Black Hat panel as the format model.
Step 2: Recruit three or four panelists from the roles they already convene: a CISO, a head of AppSec, a security founder, and an investor.
Ask them to bring one recent exploit, one control failure, and one unresolved question about agent identity, open versus closed models, boundary policy enforcement, and human-in-the-loop control.
Step 3: Have Wang moderate and advise in two working sessions.
In those sessions, pressure-test the vendor’s product narrative against the secure-by-design argument in the Corridor post and the untrusted-agent framework in the 1Password post.
The vendor should not pitch during the panel; afterward, it should publish a short technical brief naming Wang as moderator and advisor, covering agent identity, runtime threats such as prompt injection and model theft, and where human control must remain.
Step 4: Measure qualified attendance, practitioner questions submitted, brief downloads, and follow-up meetings with security leaders—not follower count.
Step 5: Discuss availability and terms directly, including vendor-sponsored work, advisory scope, conflicts and exclusivity, approval rights, and any audience or engagement data the vendor needs before committing budget.
Choose Ronen Slavin when a campaign needs someone who can argue the remediation side of AI agent security in product terms, rather than host the conversation.
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The posts examine the same buyer problem from the defender's operational angle: AI writes code faster than humans review it; the 2026 Verizon DBIR shows vulnerability exploitation passing stolen credentials as the top initial access vector, median time-to-patch up 34% year over year, and supply chain involvement in 48% of breaches; and Forrester's first Agentic Development Security Landscape names the category being built to answer it.
Slavin frames the architectural response as a context graph, an action layer with verifiable outcomes, and trust controls that hold when the agent is wrong, backed by a Mythos resource hub and guides on what to do before public model releases.
If the vendor sells triage, exploitability confirmation, or closing risk at AI speed, an advisor already fluent in that stack can pressure-test the roadmap faster than a general moderator.
Slavin's posts center on one company's positioning, making a competing vendor's involvement raise conflict and exclusivity questions.
They show category analysis and a resource hub, not a hosted practitioner panel, so a vendor seeking an unscripted CISO-level session would get less from this route.
The posts include no audience figures or statement on vendor-sponsored work; ask about reach and commercial availability before proceeding.
Choose Clint Gibler when the campaign aims to reach working practitioners through an established reading habit rather than a single event.
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The two tl;dr sec issues reviewed curate research across AppSec, cloud security, supply chain, blue team, red team, and AI plus security, with concrete items such as why VMs will not contain cyber-capable agents, MCP configuration poisoning, a password-spraying campaign against AWS root accounts across 150-plus organizations, and autonomous AI agents breaking into online retailers for about $25 per target. For a vendor that wants its agent-security research alongside the links practitioners already scan each week, with a steady cadence rather than one panel, the format fits the same audience the AI agent security brief is aimed at.
Curation is not advising.
The issues point to other people's work rather than laying out a developed position on agent identity, runtime protection, or boundary policy enforcement that a vendor could use to stress-test its product narrative.
The posts show a newsletter author and topic scanner, not a conference host or hands-on adviser.
A vendor choosing this route trades depth of platform judgment for recurring topical placement; ask about format, sponsorship terms, and audience detail before booking.
Choose Motti Sorani when the vendor needs a founder-level voice on why security work stops short of fixing anything, told through a personal origin story.
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One post traces learning assembly by reading computer viruses and building a virus generator as an intellectual challenge, before offensive security had that name; the other makes the remediation case directly: teams do not need more alerts, they need fixes that fit into development workflows, with a telecom-style last-mile analogy for the hardest part of delivery.
The co-founder conversation format with Gil Regev gives the campaign a ready-made why-this-matters segment.
For a vendor selling remediation automation or an application security workflow product, that combination of story and argument is more directly usable than a broader AI-security panel.
Sorani's posts focus on detection-to-remediation return on investment, not agent identity or runtime AI threats, so a vendor whose product is specifically AI agent security would be buying adjacent credibility.
The vocabulary a campaign on AI agent security needs, such as treating each agent as untrusted or enforcing policy at the boundary, appears in Nancy Wang's posts, not these two.
The format is a founder-led conversation rather than a moderated practitioner panel.
Ask whether Sorani can bring the network of CISOs and AppSec leaders a speaker-led event depends on.