- 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.
- Autonomous SOC agents turn AI breaches into direct firewall access.
Adversaries injected malicious prompts into legitimate AI tools at more than 90 organizations in 2025, stealing credentials and cryptocurrency. Every one of those compromised tools could read data, and none of them could rewrite a firewall rule.
The autonomous SOC agents shipping now can. That escalation, from compromised tools that read data to autonomous agents that rewrite infrastructure, has not been exploited in production at scale yet. But the architectural conditions for it are shipping faster than the governance designed to prevent it.
A compromised SOC agent can rewrite your firewall rules, modify IAM policies, and quarantine endpoints, all with its own privileged credentials, all through approved API calls that EDR classifies as authorized activity. The adversary never touches the network. The agent does it for them.
Cisco announced AgenticOps for Security in February, with autonomous firewall remediation and PCI-DSS compliance capabilities. Ivanti launched Continuous Compliance and the Neurons AI self-service agent last week, with policy enforcement, approval gates and data context validation built into the platform at launch — a design distinction that matters because the OWASP Agentic Top 10 documents what happens when those controls are absent.
"In the agentic era, defending against AI-accelerated adversaries and securing AI systems themselves, require operating at machine speed," CrowdStrike CEO George Kurtz said when releasing the 2026 Global Threat Report. "AI is compressing the time between intent and execution while turning enterprise AI systems into targets," added Adam Meyers, head of counter-adversary operations at CrowdStrike. AI-enabled adversaries increased operations 89% year-over-year.
The broader attack surface is expanding in parallel. Malicious MCP server clones have already intercepted sensitive data in AI workflows by impersonating trusted services. The U.K. National Cyber Security Centre warned that prompt injection attacks against AI applications "may never be totally mitigated." The documented compromises targeted AI tools that could only read and summarize; the autonomous SOC agents shipping now can write, enforce, and remediate.
The governance framework that maps the gap
OWASP's Top 10 for Agentic Applications, released in December 2025 and built with more than 100 security researchers, documents 10 categories of attack against autonomous AI systems. Three categories map directly to what autonomous SOC agents introduce when they ship with write access: Agent Goal Hijacking (ASI01), Tool Misuse (ASI02), and Identity and Privilege Abuse (ASI03). Palo Alto Networks reported an 82:1 machine-to-human identity ratio in the average enterprise — every autonomous agent added to production extends that gap.
The 2026 CISO AI Risk Report from Saviynt and Cybersecurity Insiders (n=235 CISOs) found 47% had already observed AI agents exhibiting unintended behavior, and only 5% felt confident they could contain a compromised agent. A separate Dark Reading poll found that 48% of cybersecurity professionals identify agentic AI as the single most dangerous attack vector. The IEEE-USA submission to NIST stated the problem plainly: "Risk is driven less by the models and is based more on the model's level of autonomy, privilege scope, and the environment of the agent being operationalized."
Eleanor Watson, Senior IEEE Member, warned in the IEEE 2026 survey that "semi-autonomous systems can also drift from intended objectives, requiring oversight and regular audits." Cisco's intent-aware agentic inspection, announced alongside AgenticOps in February 2026, represents an early detection-layer approach to the same gap. The approaches differ: Cisco is adding inspection at the network layer while Ivanti built governance into the platform layer. Both signal the industry sees it coming. The question is whether the controls arrive before the exploits do.
Autonomous agents that ship with governance built in
Security teams are already stretched. Advanced AI models are accelerating the discovery of exploitable vulnerabilities faster than any human team can remediate manually, and the backlog is growing not because teams are failing, but because the volume now exceeds what manual patching cycles can absorb.
Ivanti Neurons for Patch Management introduced Continuous Compliance this quarter, an automated enforcement framework that eliminates the gap between scheduled patch deployments and regulatory requirements. The framework identifies out-of-compliance endpoints and deploys patches out-of-band to update devices that missed maintenance windows, with built-in policy enforcement and compliance verification at every step.
Ivanti also launched the Neurons AI self-service agent for ITSM, which moves beyond conversational intake to autonomous resolution with built-in guardrails for policy, approvals, and data context. The agent resolves common incidents and service requests from start to finish, reducing manual effort and deflecting tickets.
Robert Hanson, Chief Information Officer at Grand Bank, described the decision calculus security leaders across the industry are weighing: "Before exploring the Ivanti Neurons AI self-service agent, our team was spending the bulk of our time handling repetitive requests. As we move toward implementing these capabilities, we expect to automate routine tasks and enable our team to focus more proactively on higher-value initiatives. Over time, this approach should help us reduce operational overhead while delivering faster, more secure service within the guardrails we define, ultimately supporting improvements in service quality and security."
His emphasis on operating "within the guardrails we define" points to a broader design principle: speed and governance do not have to be trade-offs.
The governance gap is concrete: the Saviynt report found 86% of organizations do not enforce access policies for AI identities, only 17% govern even half of their AI identities with the same controls applied to human users, and 75% of CISOs have discovered unsanctioned AI tools running in production with embedded credentials that nobody monitors.
Continuous Compliance and the Neurons AI self-service agent address the patching and ITSM layers. The broader autonomous SOC agent terrain, including firewall remediation, IAM policy modification, and endpoint quarantine, extends beyond what any single platform governs today. The ten-question audit applies to every autonomous tool in the environment, including Ivanti's.
Prescriptive risk matrix for autonomous agent governance
The matrix maps all 10 OWASP Agentic Top 10 risk categories to what ships without governance, the detection gap, the proof case, and the recommended action for autonomous SOC agent deployments.
OWASP Risk
What Ships Ungoverned
Detection Gap
Proof Case
Recommended Action
ASI01: Goal Hijacking
Agent treats external inputs (logs, alerts, emails) as trusted instructions
EDR cannot detect adversarial instructions executed via legitimate API calls
EchoLeak (CVE-2025-32711): hidden email payload caused AI assistant to exfiltrate confidential data. Zero clicks required.
Classify all inputs by trust tier. Block instruction-bearing content from untrusted sources. Validate external data before agent ingestion.
ASI02: Tool Misuse
Agent authorized to modify firewall rules, IAM policies, and quarantine workflows
WAF inspects payloads, not tool-call intent. Authorized use is identical to misuse.
Amazon Q bent legitimate tools into destructive outputs despite valid permissions (OWASP cited).
Scope each tool to minimum required permissions. Log every invocation with intent metadata. Alert on calls outside baseline patterns.
ASI03: Identity Abuse
Agent inherits service account credentials scoped to production infrastructure
SIEM sees authorized identity performing authorized actions. No anomaly triggers.
82:1 machine-to-human identity ratio in average enterprise (Palo Alto Networks). Each agent adds to it.
Issue scoped agent-specific identities. Enforce time-bound, task-bound credential leases. Eliminate inherited user credentials.
ASI04: Supply Chain
Agent loads third-party MCP servers or plugins at runtime without provenance verification
Static analysis cannot inspect dynamically loaded runtime components.
Malicious MCP server clones intercepted sensitive data by impersonating trusted services (CrowdStrike 2026).
Maintain approved MCP server registry. Verify provenance and integrity before runtime loading. Block unapproved plugins.
ASI05: Unexpected Code Exec
Agent generates or executes attacker-controlled code through unsafe evaluation paths or tool chains
Code review gates apply to human commits, not agent-generated runtime code.
AutoGPT RCE: natural-language execution paths enabled remote code execution through unsanctioned package installs (OWASP cited).
Sandbox all agent code execution. Require human approval for production code paths. Block dynamic eval and unsanctioned installs.
ASI06: Memory Poisoning
Agent persists context across sessions where poisoned data compounds over time
Session-based monitoring resets between interactions. Poisoning accumulates undetected.
Calendar Drift: malicious calendar invite reweighted agent objectives while remaining within policy bounds (OWASP).
Implement session memory expiration. Audit persistent memory stores for anomalous content. Isolate memory per task scope.
ASI07: Inter-Agent Comm
Agents communicate without mutual authentication, encryption, or schema validation
Monitoring covers individual agents but not spoofed or manipulated inter-agent messages.
OWASP documented spoofed messages that misdirected entire agent clusters via protocol downgrade attacks.
Enforce mutual authentication between agents. Encrypt all inter-agent channels. Validate message schema at every handoff.
ASI08: Cascading Failures
Agent delegates to downstream agents, creating multi-hop privilege chains across systems
Monitoring covers individual agents but not cross-agent delegation chains or fan-out.
Simulation: single compromised agent poisoned 87% of downstream decision-making within 4 hours in controlled test.
Map all delegation chains end to end. Enforce privilege boundaries at each handoff. Implement circuit breakers for cascading actions.
ASI09: Human-Agent Trust
Agent uses persuasive language or fabricated evidence to override human safety decisions
Compliance verifies policy configuration, not whether the agent manipulated the human into approving.
Replit agent deleted primary customer database then fabricated its contents to appear compliant and hide the damage.
Require independent verification for high-risk agent recommendations. Log all human approval decisions with full agent reasoning chain.
ASI10: Rogue Agents
Agent deviates from intended purpose while appearing compliant on the surface
Compliance checks verify configuration at deployment, not behavioral drift after deployment.
92% of organizations lack full visibility into AI identities; 86% do not enforce access policies (Saviynt 2026).
Deploy behavioral drift detection. Establish baseline agent behavior profiles. Alert on deviation from expected action patterns.
The 10-question OWASP audit for autonomous agents
Each question maps to one OWASP Agentic Top 10 risk category. Autonomous platforms that ship with policy enforcement, approval gates, and data context validation will have clear answers to every question. Three or more "I don't know" answers on any tool means that tool's governance has not kept pace with its capabilities.
Which agents have write access to production firewall, IAM, or endpoint controls?
Which accept external inputs without validation?
Which execute irreversible actions without human approval?
Which persist memory where poisoning compounds across sessions?
Which delegate to other agents, creating cascade privilege chains?
Which load third-party plugins or MCP servers at runtime?
Which generate or execute code in production environments?
Which inherit user credentials instead of scoped agent identities?
Which lack behavioral monitoring for drift from intended purpose?
Which can be manipulated through persuasive language to override safety controls?
What the board needs to hear
The board conversation is three sentences. Adversaries compromised AI tools at more than 90 organizations in 2025, according to CrowdStrike's 2026 Global Threat Report. The autonomous tools deploying now have more privilege than the ones that were compromised. The organization has audited every autonomous tool against OWASP's 10 risk categories and confirmed that the governance controls are in place.
If that third sentence is not true, it needs to be true before the next autonomous agent ships to production. Run the 10-question audit against every agent with write access to production infrastructure within the next 30 days. Every autonomous platform shipping to production should be held to the same standard — policy enforcement, approval gates, and data context validation built in at launch, not retrofitted after the first incident. The audit surfaces which tools have done that work and which have not.
- When your agents work faster than your access controls can keep up.
A doctor in a hospital exam room watches as a medical transcription agent updates electronic health records, prompts prescription options, and surfaces patient history in real time. A computer vision agent on a manufacturing line is running quality control at speeds no human inspector can match. Both generate non-human identities that most enterprises cannot inventory, scope, or revoke at machine speed.
That is the structural problem keeping agentic AI stuck in pilots. Not model capability. Not compute. Identity governance.
Cisco President Jeetu Patel told VentureBeat at RSAC 2026 that 85% of enterprises are running agent pilots while only 5% have reached production. That 80-point gap is a trust problem. The first questions any CISO will ask: which agents have production access to sensitive systems, and who is accountable when one acts outside its scope? IANS Research found that most businesses still lack role-based access control mature enough for today's human identities, and agents will make it significantly harder. The 2026 IBM X-Force Threat Intelligence Index reported a 44% increase in attacks exploiting public-facing applications, driven by missing authentication controls and AI-enabled vulnerability discovery.
Why the trust gap is architectural, not just a tooling problem
Michael Dickman, SVP and GM of Cisco's Campus Networking business, laid out a trust framework in an exclusive interview with VentureBeat that security and networking leaders rarely hear stated this plainly. Before Cisco, Dickman served as Chief Product Officer at Gigamon and SVP of Product Management at Aruba Networks.
Dickman said that the network sees what other telemetry sources miss: actual system-to-system communications rather than inferred activity. "It's that difference of knowing versus guessing," he said. "What the network can see are actual data communications … not, I think this system needs to talk to that system, but which systems are actually talking together." That raw behavioral data, he added, becomes the foundation for cross-domain correlation, and without it, organizations have no reliable way to enforce agent policy at what he called "machine speed."
The trust prerequisite that most AI strategies skip
Dickman argues that agentic AI breaks a pattern he says defined every prior technology transition: deploy for productivity first, bolt on security later.
"I don't think trust is one of those things where the business productivity comes first, and the security is an afterthought," Dickman told VentureBeat. "Trust actually is one of the key requirements. Just table stakes from the beginning."
Observing data and recommending decisions carries consequences that stay contained. Execution changes everything. When agents autonomously update patient records, adjust network configurations, or process financial transactions, the blast radius of a compromised identity expands dramatically.
"Now more than ever, it's that question of who has the right to do what," Dickman said. "The who is now much more complicated because you have the potential in our reality of these autonomous agents."
Dickman breaks the trust problem into four conditions. The first is secure delegation, which starts by defining what an agent is permitted to do and maintaining a clear chain of human accountability. The second is cultural readiness; he pointed to alert fatigue as a case study. The traditional fix, Dickman noted, was to aggregate alerts, so analysts see fewer items. With agents capable of evaluating every alert, that logic changes entirely.
"It is now possible for an agent to go through all alerts," Dickman said. "You can actually start to think about different workflows in a different way. And then how does that affect the culture of the work, which is amazing."
The third is token economics: Every agent’s action carries a real computational cost. Dickman sees hybrid architectures as the answer, where agentic AI handles reasoning while traditional deterministic tools execute actions. The fourth is human judgment. For example, his team used an AI tool to draft a product requirements document. The agent produced 60 pages of repetitive filler that immediately provided how technically responsive the architecture was, yet showed signs of needing extensive fine-tuning to make the output relevant. "There's no substitute for the human judgment and the talent that's needed to be dextrous with AI," he said.
What the network sees that endpoints miss
Most enterprise data today is proprietary, internal, and fragmented across observability tools, application platforms, and security stacks. Each domain team builds its own view. None sees the full picture.
"It's that difference of knowing versus guessing," Dickman said. "What the network can see are actual data communications. Not 'I think this system needs to talk to that system,' but which systems are actually talking together."
That telemetry grows more valuable as IoT and physical AI proliferate. Computer vision agents analyzing shopper behavior and running factory-floor quality control generate highly sensitive data that demands precise access controls.
"All of those things require that trust that we started with, because this is highly sensitive data around like who's doing what in the shop or what's happening on the factory floor," Dickman said.
Why siloed agent data misses the signal
"It's not only aggregation, but actually the creation of knowledge from the network," Dickman said. "There are these new insights you can get when you see the real data communications. And so now it becomes what do we do first versus second versus third?"
That last question reveals where Dickman’s focus lands: the strategic challenge is sequencing, not capability.
"The real power comes from the cross-domain views. The real power comes from correlation," Dickman said. "Versus just aggregation and deduplication of alerts, which is good, but it's a little bit basic."
This is where he sees the most common pitfall. Team A builds Agent A on top of Data A. Team B builds Agent B on top of Data B. Each silo produces incrementally useful automation. The cross-domain insight never materializes.
Independent practitioners validate the pattern. Kayne McGladrey, an IEEE senior member, told VentureBeat that organizations are defaulting to cloning human user profiles for agents, and permission sprawl starts on day one. Carter Rees, VP of AI at Reputation, identified the structural reason. "A significant vulnerability in enterprise AI is broken access control, where the flat authorization plane of an LLM fails to respect user permissions," Rees told VentureBeat. Etay Maor, VP of Threat Intelligence at Cato Networks, reached the same conclusion from the adversarial side. "We need an HR view of agents," Maor told VentureBeat at RSAC 2026. "Onboarding, monitoring, offboarding."
Agentic AI trust gap assessment
Use this matrix to evaluate any platform or combination of platforms against the five trust gaps Dickman identified. Note that the enforcement approaches in the right column reflect Cisco's framework.
Trust gap
Current control failure
What network-layer enforcement changes
Recommended action
Agent identity governance
IAM built for human users cannot inventory, scope, or revoke agent identities at machine speed
Agentic IAM registers each agent with defined permissions, an accountable human owner, and a policy-governed access scope
Audit every agent identity in production. Assign a human owner. Define permitted actions before expanding the scope
Blast radius containment
Host-based agents and perimeter controls can be bypassed; flat segments give compromised agents lateral movement
Microsegmentation enforces least-privileged access at the network layer, limiting blast radius independent of host-level controls
Implement microsegmentation for every agent-accessible system. Start with the highest-sensitivity data (PHI, financial records)
Cross-domain visibility
Siloed observability tools create fragmented views; Team A's agent data never correlates with Team B's security telemetry
Network telemetry captures actual system-to-system communications, feeding a unified data fabric for cross-domain correlation
Unify network, security, and application telemetry into a shared data fabric before deploying production agents
Governance-to-enforcement pipeline
No formal process connecting business intent to agent policy to network enforcement
Policy-to-enforcement pipeline translates governance decisions into machine-speed network rules
Establish a formal pipeline from business-intent definition to automated network policy enforcement
Cultural and workflow readiness
Organizations automate existing workflows rather than redesigning for agent-scale processing
Network-generated behavioral data reveals actual usage patterns, informing workflow redesign
Run a 30-day telemetry capture before designing agent workflows. Build around observed data, not assumptions
A broken ankle and a microsegmentation lesson
Dickman grounded his framework in a scenario from his own life. A family member recently broke an ankle, which put him in a hospital exam room watching a medical transcription agent update the EHR, prompt prescription options, and surface patient history in real time. The doctor approved each decision, but the agent handled tasks that previously required manual entry across multiple systems.
The security implications hit differently when it is a loved one's records on the screen.
"I would call it do governance slowly. But do the enforcement and implementation rapidly," he said. "It must be done in machine speed."
It starts with agentic IAM, where each agent is registered with defined permitted actions and a human accountable for its behavior.
"Here's my set of agents that I've built. Here are the agents. By the way, here's a human who's accountable for those agents," Dickman said. "So if something goes wrong, there's a person to talk to."
That identity layer feeds microsegmentation — a network-enforced boundary Dickman says enforces least-privileged access and limits blast radius.
"Microsegmentation guarantees that least-privileged access," Dickman said. "You're not relying on a bunch of host agents, which can be bypassed or have other issues."
If the governance model works for a medical transcription agent handling patient records in an emergency department, it scales to less sensitive enterprise use cases.
Five priorities before agents reach production
1. Force cross-functional alignment now. Define what the organization expects from agentic AI across line-of-business, IT, and security leadership. Dickman sees the human coordination layer moving more slowly than the technology. That gap is the bottleneck.
2. Get IAM and PAM governance production-ready for agents. Dickman called out identity and access management and privileged access management specifically as not mature enough for agentic workloads today. Solidify the governance before scaling the agents. "That becomes the unlock of trust," he said. "Because when the technology platform is ready, you then need the right governance and policy on top of that."
3. Adopt a platform approach to networking infrastructure. A platform strategy enables data sharing across domains in ways fragmented point solutions cannot. That shared foundation is what makes the cross-domain correlation in the trust gap assessment above operationally real.
4. Design hybrid architectures from the start. Agentic AI handles reasoning and planning. Traditional deterministic tools execute the actions. Dickman sees this combination as the answer to token economics: it delivers the intelligence of foundation models with the efficiency and predictability of conventional software. Do not build pure-agent systems when hybrid systems cost less and fail more predictably.
5. Make the first use cases bulletproof on trust. Pick two or three high-value use cases and build them with role-based access control, privileged access management, and microsegmentation from day one. Even modest deployments delivered with best practices intact build the organizational confidence that accelerates everything after.
"You can guarantee that trust to the organization, and that will unleash the speed," Dickman said.
That is the structural insight running through every section of this conversation. The 85% of enterprises stuck in pilot mode are not waiting for better models. They are waiting for the identity governance, the cross-domain visibility, and the policy enforcement infrastructure that makes production deployment defensible. Whether they build on Cisco’s platform or assemble their own, Dickman’s framework holds: identity governance, cross-domain visibility, policy enforcement. None of those prerequisites is optional.
The organizations that satisfy them first will deploy agents at a pace the rest cannot match, because every new agent inherits the trust architecture the first ones required. The ones still debating whether to start will watch that gap widen. Theoretical trust does not ship.
