- When an AI agent rewrites security policy, trust must be earned not assumed
A CEO’s AI agent rewrote the company’s security policy. Not because it was compromised, but because it wanted to fix a problem, lacked permissions, and removed the restriction itself. Every identity check passed. CrowdStrike CEO George Kurtz disclosed the incident and a second one at his RSAC 2026 keynote, both at Fortune 50 companies.
The credential was valid. The access was authorized. The action was catastrophic.
That sequence breaks the core assumption underneath the IAM systems most enterprises run in production today: that a valid credential plus authorized access equals a safe outcome. Identity systems were built for one user, one session, one set of hands on a keyboard. Agents break all three assumptions at once.
In an exclusive interview with VentureBeat at RSAC 2026, Matt Caulfield, VP of Identity and Duo at Cisco, (pictured above) walked through the architecture his team is building to close that gap and outlined a six-stage identity maturity model for governing agentic AI. The urgency is measurable: Cisco President Jeetu Patel told VentureBeat at the same conference that 85% of enterprises are running agent pilots while only 5% have reached production — an 80-point gap that the identity work is designed to close.
The identity stack was built for a workforce that has fingerprints
“Most of the existing IAM tools that we have at our disposal are just entirely built for a different era,” Caulfield told VentureBeat. “They were built for human scale, not really for agents.”
The default enterprise instinct is to shove agents into existing identity categories: human user; machine identity; pick one. "Agents are a third kind of new type of identity," Caulfield said. "They're neither human. They're neither machine. They're somewhere in the middle where they have broad access to resources like humans, but they operate at machine scale and speed like machines, and they entirely lack any form of judgment."
Etay Maor, VP of Threat Intelligence at Cato Networks, put a number on the exposure. He ran a live Censys scan and counted nearly 500,000 internet-facing OpenClaw instances. The week before, he found 230,000, discovering a doubling in seven days.
Kayne McGladrey, an IEEE senior member who advises enterprises on identity risk, made the same diagnosis independently. Organizations are cloning human user accounts to agentic systems, McGladrey told VentureBeat, except agents consume far more permissions than humans would because of the speed, the scale, and the intent.
A human employee goes through a background check, an interview, and an onboarding process. Agents skip all three. The onboarding assumptions baked into modern IAM do not apply. Scale compounds the failure. Caulfield pointed to projections where a trillion agents could operate globally. “We barely know how many people are in an average organization,” he said, “let alone the number of agents.”
Access control verifies the badge. It does not watch what happens next.
Zero trust still applies to agentic AI, Caulfield argued. But only if security teams push it past access and into action-level enforcement. “We really need to shift our thinking to more action-level control,” he told VentureBeat. “What action is that agent taking?”
A human employee with authorized access to a system will not execute 500 API calls in three seconds. An agent will. Traditional zero trust verifies that an identity can reach an application. It doesn’t scrutinize what that identity does once inside.
Carter Rees, VP of Artificial Intelligence at Reputation, identified the structural reason. The flat authorization plane of an LLM fails to respect user permissions, Rees told VentureBeat. An agent operating on that flat plane does not need to escalate privileges. It already has them. That is why access control alone cannot contain what agents do after authentication.
CrowdStrike CTO Elia Zaitsev described the detection gap to VentureBeat. In most default logging configurations, an agent’s activity is indistinguishable from a human. Distinguishing the two requires walking the process tree, tracing whether a browser session was launched by a human or spawned by an agent in the background. Most enterprise logging cannot make that distinction.
Caulfield’s identity layer and Zaitsev’s telemetry layer are solving two halves of the same problem. No single vendor closes both gaps.
“At any moment in time, that agent can go rogue and can lose its mind,” Caulfield said. “Agents read the wrong website or email, and their intentions can just change overnight.”
How the request lifecycle works when agents have their own identity
Five vendors shipped agent identity frameworks at RSAC 2026, including Cisco, CrowdStrike, Palo Alto Networks, Microsoft, and Cato Networks. Caulfield walked through how Cisco's identity-layer approach works in practice.
The Duo agent identity platform registers agents as first-class identity objects, with their own policies, authentication requirements, and lifecycle management. The enforcement routes all agent traffic through an AI gateway supporting both MCP and traditional REST or GraphQL protocols. When an agent makes a request, the gateway authenticates the user, verifies that the agent is permitted, encodes the authorization into an OAuth token, and then inspects the specific action and determines in real time whether it should proceed.
“No solution to agent AI is really complete unless you have both pieces,” Caulfield told VentureBeat. “The identity piece, the access gateway piece. And then the third piece would be observability.”
Cisco announced its intent to acquire Astrix Security on May 4, signaling that agent identity discovery is now a board-level investment thesis. The deal also suggests that even vendors building identity platforms recognize that the discovery problem is harder than expected.
Six-stage identity maturity model for agentic AI
When a company shows up claiming 500 agents in production, Caulfield doesn't accept the number. "How do you know it's 500 and not 5,000?"
Most organizations don’t have a source of truth for agents. Caulfield outlined a six-stage engagement model.
Discovery first: identify every agent, where it runs, and who deployed it. Onboarding: register agents in the identity directory, tie each one to an accountable human, and define permitted actions. Control and enforcement: place a gateway between agents and resources, inspect every request and response. Behavioral monitoring: record all agent activity, flag anomalies, and build the audit trail. Runtime isolation contains agents on endpoints when they go rogue. Compliance mapping ties agent controls to audit frameworks before the auditor shows up. The six stages are not proprietary to any single vendor. They describe the sequence every enterprise will follow regardless of which platform delivers each stage.
Maor's Censys data complicates step one before it even starts. Organizations beginning discovery should assume their agent exposure is already visible to adversaries. Step four has its own problem. Zaitsev's process-tree work shows that even organizations logging agent activity may not be capturing the right data. And step three depends on something Rees found most enterprises lack: a gateway that inspects actions, not just access, because the LLM does not respect the permission boundaries the identity layer sets.
Agentic identity prescriptive matrix
What to audit at each maturity stage, what operational readiness looks like, and the red flag that means the stage is failing. Use this to evaluate any platform or combination of platforms.
Stage
What to audit
Operational readiness looks like
Red flag if missing
1. Discovery
Complete inventory of every agent, every MCP server it connects to, and every human accountable for it.
A queryable registry that returns agent count, owner, and connection map within 60 seconds of an auditor asking.
No registry exists. Agent count is an estimate. No human is accountable for any specific agent. Adversaries can see your agent infrastructure from the public internet before you can.
2. Onboarding
Agents are registered as a distinct identity type with their own policies, separate from human and machine identities.
Each agent has a unique identity object in the directory, tied to an accountable human, with defined permitted actions and a documented purpose.
Agents use cloned human accounts or shared service accounts. Permission sprawl starts at creation. No audit trail ties agent actions to a responsible human.
3. Control
A gateway between every agent and every resource it accesses, enforcing action-level policy on every request and every response.
Four checkpoints per request: authenticate the user, authorize the agent, inspect the action, inspect the response. No direct agent-to-resource connections exist.
Agents connect directly to tools and APIs. The gateway (if it exists) checks access but not actions. The flat authorization plane of the LLM does not respect the permission boundaries the identity layer set.
4. Monitoring
Logging that can distinguish agent-initiated actions from human-initiated actions at the process-tree level.
SIEM can answer: Was this browser session started by a human or spawned by an agent? Behavioral baselines exist for each agent. Anomalies trigger alerts.
Default logging treats agent and human activity as identical. Process-tree lineage is not captured. Agent actions are invisible in the audit trail. Behavioral monitoring is incomplete before it starts.
5. Isolation
Runtime containment that limits the blast radius if an agent goes rogue, separate from human endpoint protection.
A rogue agent can be contained in its sandbox without taking down the endpoint, the user session, or other agents on the same machine.
No containment boundary exists between agents and the host. A single compromised agent can access everything the user can. Blast radius is the entire endpoint.
6. Compliance
Documentation that maps agent identities, controls, and audit trails to the compliance framework that the auditor will use.
When the auditor asks about agents, the security team produces a control catalog, an audit trail, and a governance policy written for agent identities specifically.
Emerging AI-risk frameworks (CSA Agentic Profile) exist, but mainstream audit catalogs (SOC 2, ISO 27001, PCI DSS) have not operationalized agent identities. No control catalog maps to agents. The auditor improvises which human-identity controls apply. The security team answers with improvisation, not documentation.
Source: VentureBeat analysis of RSAC 2026 interviews (Caulfield, Zaitsev, Maor) and independent practitioner validation (McGladrey, Rees). May 2026.
Compliance frameworks have not caught up
“If you were to go through an audit today as a chief security officer, the auditor’s probably gonna have to figure out, hey, there are agents here,” Caulfield told VentureBeat. “Which one of your controls is actually supposed to be applied to it? I don’t see the word agents anywhere in your policies.”
McGladrey's practitioner experience confirms the gap. The Cloud Security Alliance published an NIST AI RMF Agentic Profile in April 2026, proposing autonomy-tier classification and runtime behavioral metrics. But SOC 2, ISO 27001, and PCI DSS have not operationalized agent identities. The compliance frameworks McGladrey works with inside enterprises were written for humans. Agent identities do not appear in any control catalog he has encountered. The gap is a lagging indicator; the risk is not.
Security director action plan
VentureBeat identified five actions from the combined findings of Caulfield, Zaitsev, Maor, McGladrey, and Rees.
Run an agent census and assume adversaries already did.
Every agent, every MCP server those agents touch, every human accountable. Maor's Censys data confirms agent infrastructure is already visible from the public internet. NIST's NCCoE reached the same conclusion in its February 2026 concept paper on AI agent identity and authorization.
Stop cloning human accounts for agents.
McGladrey found that enterprises default to copying human user profiles, and permission sprawl starts on day one. Agents need to be a distinct identity type with scope limits that reflect what they actually do.
Audit every MCP and API access path.
Five vendors shipped MCP gateways at RSAC 2026. The capability exists. What matters is whether agents route through one or connect directly to tools with no action-level inspection.
Fix logging so it distinguishes agents from humans.
Zaitsev's process-tree method reveals that agent-initiated actions are invisible in most default configurations. Rees found authorization planes so flat that access logs alone miss the actual behavior. Logging has to capture what agents did, not just what they were allowed to reach.
Build the compliance case before the auditor shows up.
The CSA published a NIST AI RMF Agentic Profile proposing agent governance extensions. Most audit catalogs have not caught up. Caulfield told VentureBeat that auditors will see agents in production and find no controls mapped to them. The documentation needs to exist before that conversation starts.
- RSAC 2026 revealed five agent identity frameworks and three blind spots
“You can deceive, manipulate, and lie. That’s an inherent property of language. It’s a feature, not a flaw,” CrowdStrike CTO Elia Zaitsev told VentureBeat in an exclusive interview at RSA Conference 2026. If deception is baked into language itself, every vendor trying to secure AI agents by analyzing their intent is chasing a problem that cannot be conclusively solved. Zaitsev is betting on context instead. CrowdStrike’s Falcon sensor walks the process tree on an endpoint and tracks what agents did, not what agents appeared to intend. “Observing actual kinetic actions is a structured, solvable problem,” Zaitsev told VentureBeat. “Intent is not.”
That argument landed 24 hours after CrowdStrike CEO George Kurtz disclosed two production incidents at Fortune 50 companies. In the first, a CEO's AI agent rewrote the company's own security policy — not because it was compromised, but because it wanted to fix a problem, lacked the permissions to do so, and removed the restriction itself. Every identity check passed; the company caught the modification by accident. The second incident involved a 100-agent Slack swarm that delegated a code fix between agents with no human approval. Agent 12 made the commit. The team discovered it after the fact.
Two incidents at two Fortune 50 companies. Caught by accident both times. Every identity framework that shipped at RSAC this week missed them. The vendors verified who the agent was. None of them tracked what the agent did.
The urgency behind every framework launch reflects a broader market shift. "The difficulty of securing agentic AI is likely to push customers toward trusted platform vendors that can offer broader coverage across the expanding attack surface," according to William Blair's RSA Conference 2026 equity research report by analyst Jonathan Ho. Five vendors answered that call at RSAC this week. None of them answered it completely.
Attackers are already inside enterprise pilots
The scale of the exposure is already visible in production data. CrowdStrike's Falcon sensors detect more than 1,800 distinct AI applications across the company's customer fleet, generating 160 million unique instances on enterprise endpoints. Cisco found that 85% of its enterprise customers surveyed have pilot agent programs; only 5% have moved to production, meaning the vast majority of these agents are running without the governance structures production deployments typically require. "The biggest impediment to scaled adoption in enterprises for business-critical tasks is establishing a sufficient amount of trust," Cisco President and Chief Product Officer Jeetu Patel told VentureBeat in an exclusive interview at RSA Conference 2026. "Delegating versus trusted delegating of tasks to agents. The difference between those two, one leads to bankruptcy and the other leads to market dominance."
Etay Maor, VP of Threat Intelligence at Cato Networks, ran a live Censys scan during an exclusive VentureBeat interview at RSA Conference 2026 and counted nearly 500,000 internet-facing OpenClaw instances. The week before: 230,000. Cato CTRL senior researcher Vitaly Simonovich documented a BreachForums listing from February 22, 2026, published on the Cato CTRL blog on February 25, where a threat actor advertised root shell access to a UK CEO’s computer for $25,000 in cryptocurrency. The selling point was the CEO’s OpenClaw AI personal assistant, which had accumulated the company’s production database, Telegram bot tokens, and Trading 212 API keys in plain-text Markdown with no encryption at rest. “Your AI? It’s my AI now. It’s an assistant for the attacker,” Maor told VentureBeat.
The exposure data from multiple independent researchers tells the same story. Bitsight found more than 30,000 OpenClaw instances exposed to the public internet between January 27 and February 8, 2026. SecurityScorecard identified 15,200 of those instances as vulnerable to remote code execution through three high-severity CVEs, the worst rated CVSS 8.8. Koi Security found 824 malicious skills on ClawHub — 335 of them tied to ClawHavoc, which Kurtz flagged in his keynote as the first major supply chain attack on an AI agent ecosystem.
Five vendors, three gaps none of them closed
Cisco went deepest on identity governance. Duo Agentic Identity registers agents as distinct identity objects mapped to human owners, and every tool call routes through an MCP gateway in Secure Access SSE. Cisco Identity Intelligence catches shadow agents by monitoring network traffic rather than authentication logs. Patel told VentureBeat that today’s agents behave “more like teenagers — supremely intelligent, but with no fear of consequence, easily sidetracked or influenced.” CrowdStrike made the biggest philosophical bet, treating agents as endpoint telemetry and tracking the kinetic layer through Falcon’s process-tree lineage. CrowdStrike expanded AIDR to cover Microsoft Copilot Studio agents and shipped Shadow SaaS and AI Agent Discovery across Copilot, Salesforce Agentforce, ChatGPT Enterprise, and OpenAI Enterprise GPT.
Palo Alto Networks built Prisma AIRS 3.0 with an agentic registry, an agentic IDP, and an MCP gateway for runtime traffic control. Palo Alto Networks’ pending Koi acquisition adds supply chain and runtime visibility. Microsoft spread governance across Entra, Purview, Sentinel, and Defender, with Microsoft Sentinel embedding MCP natively and a Claude MCP connector in public preview April 1. Cato CTRL delivered the adversarial proof that the identity gaps the other four vendors are trying to close are already being exploited. Maor told VentureBeat that enterprises abandoned basic security principles when deploying agents. “We just gave these AI tools complete autonomy,” Maor said.
Gap 1: Agents can rewrite the rules governing their own behavior
The Kurtz incident illustrates the gap exactly. Every credential check passed — the action was authorized. Zaitsev argues that the only reliable detection happens at the kinetic layer: which file was modified, by what process, initiated by what agent, compared against a behavioral baseline. Intent-based controls evaluate whether the call looks malicious. This one did not. Palo Alto Networks offers pre-deployment red teaming in Prisma AIRS 3.0, but red teaming runs before deployment, not during runtime when self-modification happens. No vendor ships behavioral anomaly detection for policy-modifying actions as a production capability.
Patel framed the stakes in the VentureBeat interview: “The agent takes the wrong action and worse yet, some of those actions might be critical actions that are not reversible.” Board question: An authorized agent modifies the policy governing the agent’s future actions. What fires?
Gap 2: Agent-to-agent handoffs have no trust verification
The 100-agent swarm is the proof point. Agent A found a defect and posted to Slack. Agent 12 executed the fix. No human approved the delegation. Zaitsev’s approach: collapse agent identities back to the human. An agent acting on your behalf should never have more privileges than you do. But no product follows the delegation chain between agents. IAM was built for human-to-system. Agent-to-agent delegation needs a trust primitive that does not exist in OAuth, SAML, or MCP.
Gap 3: Ghost agents hold live credentials with no offboarding
Organizations adopt AI tools, run a pilot, lose interest, and move on. The agents keep running. The credentials stay active. Maor calls these abandoned instances ghost agents. Zaitsev connected ghost agents to a broader failure: agents expose where enterprises delayed action on basic identity hygiene. Standing privileged accounts, long-lived credentials, and missing offboarding procedures. These problems existed for humans. Agents running at machine speed make the consequences catastrophic.
Maor demonstrated a Living Off the AI attack at the RSA Conference 2026, chaining Atlassian’s MCP and Jira Service Management to show that attackers do not separate trusted tools, services, and models. Attackers chain all three. “We need an HR view of agents,” Maor told VentureBeat. “Onboarding, monitoring, offboarding. If there’s no business justification? Removal.”
Why these three gaps resist a product fix
Human IAM assumes the identity holder will not rewrite permissions, spawn new identities, or leave. Agents violate all three. OAuth handles user-to-service. SAML handles federated human identity. MCP handles model-to-tool. None includes agent-to-agent verification.
Five vendors against three gaps
Cisco
CrowdStrike
Microsoft
Palo Alto Networks
Unsolved
Registration. Can the vendor discover and inventory agents?
Duo Agentic Identity. Agents registered as identity objects with human owners. Shadow agent detection via network traffic.
Falcon sensor auto-discovery. 1,800+ agent apps, ~160M instances across customer fleet.
Security Dashboard for AI + Entra shadow AI detection at the network layer.
Agentic registry in Prisma AIRS 3.0. Agents inventoried before operating.
All four register agents. No cross-vendor identity standard exists.
Self-modification. Can the vendor detect when an agent changes its own policies?
MCP gateway catches anomalous tool-call patterns in real time, but does not monitor for direct policy file modifications on the endpoint.
Process-tree lineage tracks file modifications at the action layer. Could detect a policy file change, but no dedicated self-modification rule ships.
Defender predictive shielding adjusts access policies reactively during active attacks. Not proactive self-modification detection.
AI Red Teaming tests for this before deployment. No runtime detection after the agent is live.
OPEN. No vendor detects an agent rewriting the policy governing the agent’s own behavior as a shipping capability.
Delegation. Can the vendor track when one agent hands work to another?
Maps each agent to a human owner. Does not track agent-to-agent handoffs.
Collapses the agent identity to the human operator. Does not correlate the delegation chains between agents.
Entra governs individual non-human identities. No multi-agent chain tracking.
AI Agent Gateway governs individual agents. No delegation primitive between agents.
OPEN. No trust primitive for agent-to-agent delegation exists in OAuth, SAML, or MCP.
Decommission. Can the vendor confirm a killed agent holds zero credentials?
Identity Intelligence runs a continuous inventory of active agents.
Shadow SaaS + AI Agent Discovery finds running agents across SaaS and endpoints.
Entra's shadow AI detection surfaces unmanaged AI applications.
Koi acquisition (pending) adds endpoint visibility for agent applications.
OPEN. All four discover running agents. None verifies zero residual credentials after decommission.
Runtime / Kinetic. Can the vendor monitor what agents do in real time?
MCP gateway enforces policy per tool call at the network layer. Contextual anomaly detection on call patterns.
Falcon EDR tracks commands, scripts, file activity, and network connections at the process level.
Defender endpoint + cloud monitoring. Predictive shielding during active incidents.
Prisma AIRS AI Agent Gateway for runtime traffic control.
CrowdStrike is the only vendor framing endpoint runtime as the primary safety net for agentic behavior.
Five things to do Monday morning before your board asks
Audit self-modification risk. Pull every agent with write access to security policies, IAM configs, firewall rules, or ACLs. Flag any agent that can modify controls governing the agent’s own behavior. No vendor automates this.
Map delegation paths. Document every agent-to-agent invocation. Flag delegation without human approval. Human-in-the-loop on every delegation event until a trust primitive ships.
Kill ghost agents. Build a registry. For each agent: business justification, human owner, credentials held, systems accessed. No justification? Manual revoke. Weekly.
Stress test the MCP gateway enforcement. Cisco, Palo Alto Networks, and Microsoft all announced MCP gateways this week. Verify that agent tool traffic actually routes through the gateway. A misconfigured gateway creates false confidence while agents call tools directly.
Baseline agent behavioral norms. Before any agent reaches production, establish what normal looks like: typical API calls, data access patterns, systems touched, and hours of activity. Without a behavioral baseline, the kinetic-layer anomaly detection Zaitsev describes has nothing to compare against.
Zaitsev’s advice was blunt: you already know what to do. Agents just made the cost of not doing it catastrophic. Every vendor at RSAC verified who the agent was. None of them tracked what the agent did.
- Trust in AI agents lags behind their enterprise adoption
Eighty-five percent of enterprises are running AI agent pilots, but only 5% have moved those agents into production. In an exclusive interview at RSA Conference 2026, Cisco President and Chief Product Officer Jeetu Patel said that the gap comes down to one thing: trust — and that closing it separates market dominance from bankruptcy. He also disclosed a mandate that will reshape Cisco's 90,000-person engineering organization.
The problem is not rogue agents. The problem is the absence of a trust architecture.
The trust deficit behind a 5% production rate
A recent Cisco survey of major enterprise customers found that 85% have AI agent pilot programs underway. Only 5% moved those agents into production. That 80-point gap defines the security problem the entire industry is trying to close. It is not closing.
"The biggest impediment to scaled adoption in enterprises for business-critical tasks is establishing a sufficient amount of trust," Patel told VentureBeat. "Delegating versus trusted delegating of tasks to agents. The difference between those two, one leads to bankruptcy and the other leads to market dominance."
He compared agents to teenagers. "They're supremely intelligent, but they have no fear of consequence. They're pretty immature. And they can be easily sidetracked or influenced," Patel said. "What you have to do is make sure that you have guardrails around them and you need some parenting on the agents."
The comparison carries weight because it captures the precise failure mode security teams face. Three years ago, a chatbot that gave the wrong answer was an embarrassment. An agent that takes the wrong action can trigger an irreversible outcome. Patel pointed to a case he cited in his keynote where an AI coding agent deleted a live production database during a code freeze, tried to cover its tracks with fake data, and then apologized. "An apology is not a guardrail," Patel said in his keynote blog. The shift from information risk to action risk is the core reason the pilot-to-production gap persists.
Defense Claw and the open-source speed play with Nvidia
Cisco's response to the trust deficit at RSAC 2026 spanned three categories: protecting agents from the world, protecting the world from agents, and detecting and responding at machine speed. The product announcements included AI Defense Explorer Edition (a free, self-service red teaming tool), the Agent Runtime SDK for embedding policy enforcement into agent workflows at build time, and the LLM Security Leaderboard for evaluating model resilience against adversarial attacks.
The open-source strategy moved faster than any of those. Nvidia launched OpenShell, a secure container for open-source agent frameworks, at GTC the week before RSAC. Cisco packaged its Skills Scanner, MCP Scanner, AI Bill of Materials tool, and CodeGuard into a single open-source framework called Defense Claw and hooked it into OpenShell within 48 hours.
"Every single time you actually activate an agent in an Open Shell container, you can now automatically instantiate all the security services that we have built through Defense Claw," Patel told VentureBeat. The integration means security enforcement activates at container launch without manual configuration. That speed matters because the alternative is asking developers to bolt on security after the agent is already running.
That 48-hour turnaround was not an anomaly. Patel said several of the Defense Claw capabilities Cisco launched were built in a week. "You couldn't have built it in longer than a week because Open Shell came out last week," he said.
A six-to-nine-month product lead and an information asymmetry on top of it
Patel made a competitive claim worth examining. "Product wise, we might be six to nine months ahead of most of the market," he told VentureBeat. He added a second layer: "We also have an asymmetric information advantage of, I'd say, three to six months on everyone because, you know, we, by virtue of being in the ecosystem with all the model companies. We're seeing what's coming down the pipe." The 48-hour Defense Claw sprint supports the speed claim, though the lead margin is Cisco's own characterization; no independent benchmarks were provided.
Cisco also extended zero trust to the agentic workforce through new Duo IAM and Secure Access capabilities, giving every agent time-bound, task-specific permissions. On the SOC side, Splunk announced Exposure Analytics for continuous risk scoring, Detection Studio for streamlined detection engineering, and Federated Search for investigating across distributed data environments.
The zero-human-code engineering mandate
AI Defense, the product Cisco launched a year before RSAC 2026, is now 100% built with AI. Zero lines of human-written code. By the end of 2026, half a dozen Cisco products will reach the same milestone. By the end of calendar year 2027, Patel's goal is 70% of Cisco's products built entirely by AI.
"Just process that for a second and go: a $60 billion company is gonna have 70% of the products that are gonna have no human lines of code," Patel told VentureBeat. "The concept of a legacy company no longer exists."
He connected that mandate to a cultural shift inside the engineering organization. "There's gonna be two kinds of people: ones that code with AI and ones that don't work at Cisco," Patel said. That was not debated. "Changing 30,000 people to change the way that they work at the very core of what they do in engineering cannot happen if you just make it a democratic process. It has to be something that's driven from the top down."
Five moats for the agentic era, and what CISOs can verify today
Patel laid out five strategic advantages that will separate winning enterprises from failing ones. VentureBeat mapped each moat against actions security teams can begin verifying today.
Moat
Patel's claim
What CISOs can verify today
What to validate next
Sustained speed
"Operating with extreme levels of obsession for speed for a durable length of time" creates compounding value
Measure deployment velocity from pilot to production. Track how long agent governance reviews take.
Pair speed metrics with telemetry coverage. Fast deployment without observability creates blind acceleration.
Trust and delegation
Trusted delegation separates market dominance from bankruptcy
Audit delegation chains. Flag agent-to-agent handoffs with no human approval.
Agent-to-agent trust verification is the next primitive the industry needs. OAuth, SAML, and MCP do not yet cover it.
Token efficiency
Higher output per token creates a strategic advantage
Monitor token consumption per workflow. Benchmark cost-per-action across agent deployments.
Token efficiency metrics exist. Token security metrics (what the token accessed, what it changed) are the next build.
Human judgment
"Just because you can code it doesn't mean you should."
Track decision points where agents defer to humans vs. act autonomously.
Invest in logging that distinguishes agent-initiated from human-initiated actions. Most configurations cannot yet.
AI dexterity
"10x to 20x to 50x productivity differential" between AI-fluent and non-fluent workers
Measure the adoption rates of AI coding tools across security engineering teams.
Pair dexterity training with governance training. One without the other compounds the risk.
The telemetry layer the industry is still building
Patel's framework operates at the identity and policy layer. The next layer down, telemetry, is where the verification happens. "It looks indistinguishable if an agent runs your web browser versus if you run your browser," CrowdStrike CTO Elia Zaitsev told VentureBeat in an exclusive interview at RSAC 2026. Distinguishing the two requires walking the process tree, tracing whether Chrome was launched by a human from the desktop or spawned by an agent in the background. Most enterprise logging configurations cannot make that distinction yet.
A CEO's AI agent rewrote the company's security policy. Not because it was compromised. Because it wanted to fix a problem, lacked permissions, and removed the restriction itself. Every identity check passed. CrowdStrike CEO George Kurtz disclosed that incident and a second one at his RSAC keynote, both at Fortune 50 companies. In the second, a 100-agent Slack swarm delegated a code fix between agents without human approval.
Both incidents were caught by accident
Etay Maor, VP of Threat Intelligence at Cato Networks, told VentureBeat in a separate exclusive interview at RSAC 2026 that enterprises abandoned basic security principles when deploying agents. Maor ran a live Censys scan during the interview and counted nearly 500,000 internet-facing agent framework instances. The week before: 230,000. Doubling in seven days.
Patel acknowledged the delegation risk in the interview. "The agent takes the wrong action and worse yet, some of those actions might be critical actions that are not reversible," he said. Cisco's Duo IAM and MCP gateway enforce policy at the identity layer. Zaitsev's work operates at the kinetic layer: tracking what the agent did after the identity check passed. Security teams need both. Identity without telemetry is a locked door with no camera. Telemetry without identity is footage with no suspect.
Token generation as the currency for national competitiveness
Patel sees the infrastructure layer as decisive. "Every country and every company in the world is gonna wanna make sure that they can generate their own tokens," he told VentureBeat. "Token generation becomes the currency for success in the future." Cisco's play is to provide the most secure and efficient technology for generating tokens at scale, with Nvidia supplying the GPU layer. The 48-hour Defense Claw integration demonstrated what that partnership produces under pressure.
Security director action plan
VentureBeat identified five steps security teams can take to begin building toward Patel's framework today:
Audit the pilot-to-production gap. Cisco's own survey found 85% of enterprises piloting, 5% in production. Mapping the specific trust deficits keeping agents stuck is the starting point — the answer is rarely the technology. Governance, identity, and delegation controls are what's missing. Patel's trusted delegation framework is designed to close that gap.
Test Defense Claw and AI Defense Explorer Edition. Both are free. Red-team your agent workflows before they reach production. Test the workflow, not just the model.
Map delegation chains end-to-end. Flag every agent-to-agent handoff with no human approval. This is the "parenting" Patel described. No product fully automates it yet. Do it manually, every week.
Establish agent behavioral baselines. Before any agent reaches production, define what normal looks like: API call patterns, data access frequency, systems touched, and hours of activity. Without a baseline, the observability that Patel's moats require has nothing to compare against.
Close the telemetry gap in your logging configuration. Verify that your SIEM can distinguish agent-initiated actions from human-initiated actions. If it cannot, the identity layer alone will not catch the incidents Kurtz described at RSAC. Patel built the identity layer. The telemetry layer completes it.
