NIST · SP 800-207
Zero Trust Architecture, including the principle that network location or asset ownership should not create implicit trust.
Cybersecurity was built around trusted users, trusted networks, trusted software, and trusted administrators. Attackers increasingly win by compromising those trusted layers and inheriting the access defenders already approved.
For decades, enterprise security rested on a practical assumption: some parts of the environment could be trusted.
A user inside the network was trusted more than a user outside it. A device issued by the company was trusted more than an unknown device. Software signed by a recognized vendor was trusted more than an unverified download. An administrator, identity provider, browser session, or management server was trusted because the organization needed it to operate.
That model was not irrational. It reflected a world in which applications lived inside company-owned data centers, employees worked from offices, administrative systems were reachable only from protected networks, and software dependencies were comparatively limited.
But the architecture changed.
Organizations moved their data into cloud platforms, their applications into SaaS services, their authentication into identity providers, their development into shared pipelines, and their daily work into browsers. They connected vendors, APIs, contractors, mobile devices, remote administrators, automation services, and now AI agents.
Trust did not disappear. It multiplied.
Each new relationship created another path through which authority could be inherited. An attacker who compromises one trusted layer may no longer need to defeat the controls protecting every downstream system. The attacker can simply use the access that those systems already recognize.
Implicit trust is sometimes described as an old security mistake. In reality, it was an operating model.
Businesses cannot function without delegating authority. Employees need access to applications. Servers need to communicate with databases. Administrators need tools to manage infrastructure. Software updates need permission to change installed code. Identity providers need authority to tell applications who a user is.
The problem is not that organizations trust these systems. The problem is that the consequences of compromise often exceed the purpose for which the trust was originally granted.
A help-desk account may reset credentials for thousands of users. A cloud automation identity may modify hundreds of resources. A CI/CD pipeline may push code into every production environment. A browser session may hold active access to email, finance, source code, cloud administration, and customer data at the same time.
Each of these systems is useful because it concentrates authority. That same concentration makes it valuable to an attacker.
“The attacker does not need to break every defensive layer when one trusted system can instruct the other layers to open.“
The traditional security perimeter separated an internal network from the internet. Firewalls inspected traffic crossing that boundary, while systems inside the network were often allowed to communicate more freely.
That boundary weakened as users became remote, applications moved to the cloud, and organizations adopted SaaS platforms that never lived inside the corporate network. Identity began replacing location as the primary means of granting access.
But identity did not create a new perimeter in the traditional sense. It created a distributed trust fabric.
A single identity may now be recognized by Microsoft 365, Salesforce, AWS, GitHub, ServiceNow, internal applications, VPN gateways, collaboration platforms, and third-party services. Authentication occurs in one place, while authorization is inherited across many others.
NIST defines Zero Trust around a central principle: assets and user accounts should not receive implicit trust solely because of their network location or ownership. Authentication and authorization should instead be performed in relation to the specific resource being requested.
That distinction matters. A user may be legitimately authenticated and still present unacceptable risk. The device may be unmanaged. The session may have been stolen. The requested action may be unusual. The account may be attempting to reach data it has never accessed before.
Identity tells the system who is presenting the request. It does not automatically prove that the request is safe.
Credential theft was once largely about usernames and passwords. Modern identity attacks increasingly target the mechanisms that exist after authentication: session cookies, access tokens, OAuth grants, federation certificates, device registrations, recovery processes, and privileged role assignments.
This changes the value of multi-factor authentication. MFA remains a critical control, but it cannot protect a session that has already been authenticated and stolen. It cannot revoke an overly broad OAuth permission that a user approved. It cannot detect every token forged with a compromised signing key.
MITRE ATT&CK separately documents forged web cookies and forged SAML tokens because attackers can use authentication material that applications already accept. In a Golden SAML scenario, possession of a federation token-signing certificate may allow an attacker to create SAML tokens carrying chosen identity and authorization claims.
The attacker is no longer attempting to guess the user’s password. The attacker is manufacturing the proof that the password check, MFA challenge, and identity validation already succeeded.
| Security layer | Traditional assumption | Modern attacker objective |
|---|---|---|
| Password | Possession proves the user knows the secret | Steal credentials or bypass the password entirely |
| MFA | A second factor confirms the login | Capture the authenticated session after MFA succeeds |
| Federation | A signed token proves the identity provider approved access | Steal the signing key or manipulate federation trust |
| OAuth | User consent grants a legitimate application access | Trick the user or administrator into approving malicious access |
| Recovery | Support processes restore access to the real user | Exploit the support process to take control of the identity |
The SolarWinds-era intrusions demonstrated how trust could become the mechanism of intrusion at several levels. A trusted software update provided initial access in some environments. Attackers then targeted identity infrastructure, including AD FS signing material, to extend access into cloud services.
The attack was not powerful because one vulnerability defeated every defense. It was powerful because compromise moved through systems that organizations had already authorized to distribute software, authenticate users, and connect environments.
Organizations still often treat the browser as an endpoint application. Operationally, it has become something closer to a cloud operating system.
Employees use browsers to access email, customer systems, source-code repositories, financial platforms, cloud consoles, HR records, administrative portals, collaboration tools, and AI assistants. A single browser may hold active sessions to many of these services simultaneously.
This concentration makes session theft particularly dangerous. A stolen session cookie may allow an attacker to reuse access without submitting the password again. Depending on the service and its controls, the attacker may also avoid a new MFA challenge because the original session has already passed it.
Malicious extensions create another trust problem. Extensions may receive permission to read or modify content on visited pages, interact with browser tabs, or observe user activity. Even a legitimate extension can become risky if its ownership changes, its update process is compromised, or its permissions exceed its purpose.
The browser therefore sits at the intersection of identity, data, SaaS, and user behavior. Yet many security programs still have less visibility into browser activity than into processes running directly on the endpoint.
Attackers increasingly target control planes because control planes convert one compromise into many actions.
A hypervisor manages virtual machines. A cloud console manages workloads, identities, storage, and networks. A remote monitoring and management platform administers endpoints. A backup server controls recovery data. A Kubernetes control plane governs containerized workloads. A source-code platform may connect directly to deployment pipelines.
The controller is trusted to make changes that would appear suspicious if performed by an unknown system. It may deploy software, create users, alter policies, collect credentials, restart services, or access sensitive configuration.
This means a control-plane compromise can make malicious activity look operationally legitimate. The commands may arrive through approved protocols. The software may be delivered by an authorized management agent. The account may possess the expected role.
Traditional endpoint-focused security can detect some of the resulting activity, but it may struggle to answer the more important question: why did a trusted management system issue the command?
Software supply-chain attacks exploit a simple operational reality: organizations must accept code they did not write themselves.
Developers import libraries. Build systems retrieve dependencies. Servers install updates. Browsers download extensions. Containers are assembled from base images. Cloud applications connect to third-party services.
Each step depends on a trust decision.
The package name is assumed to refer to the expected project. The repository is assumed to be authentic. The maintainer account is assumed to remain under the original owner’s control. The digital signature is assumed to represent an uncompromised signing process. The update channel is assumed to distribute only authorized code.
When one of those assumptions fails, malicious code may arrive through the same mechanism used for legitimate maintenance. Security tools see a recognized updater, an approved repository, a valid certificate, or a familiar vendor.
Trust becomes executable.
The lesson is not that organizations should stop using third-party software. That is neither practical nor desirable. The lesson is that provenance does not remove the need for verification, containment, monitoring, and recovery planning.
“A trusted update is not automatically a safe update. It is code delivered through a channel the organization has chosen not to question.“
Human users are no longer the only identities operating inside enterprise systems.
Service accounts, API keys, workload identities, automation tokens, certificates, secrets, and application registrations allow machines to act without direct human involvement. These identities often perform essential work: transferring data, deploying code, processing transactions, creating resources, or connecting business systems.
They can also be difficult to govern.
A machine identity may have no clear owner. Its credentials may remain valid for years. Its permissions may have expanded as new features were added. It may be copied into scripts, configuration files, build logs, or developer environments. Because the activity is automated, unusual behavior may be mistaken for normal system traffic.
Human accounts at least have expected work hours, devices, locations, and behavior. Machine identities may operate continuously and at high speed. Once compromised, they can perform large numbers of authorized actions before a human notices.
The implicit trust problem therefore extends beyond who is allowed to log in. It includes every non-human identity permitted to act.
AI systems introduce a new trust layer because they do more than execute predefined commands. They interpret instructions.
An AI assistant may summarize documents without taking external action. An AI agent may be connected to email, files, calendars, ticketing platforms, source-code repositories, cloud services, databases, or business applications. It may retrieve information, select a tool, construct a request, and perform an action on the user’s behalf.
The security boundary is no longer limited to code and permissions. It now includes the model’s interpretation of context.
A malicious instruction embedded in an email, webpage, document, support ticket, or retrieved dataset may attempt to influence the agent. Excessive tool permissions can turn an interpretation error into data exposure or an unauthorized action. Weak separation between untrusted content and trusted instructions can make the agent a bridge between external input and internal authority.
This does not make AI agents inherently unsafe. It means they should be governed like privileged automation—not treated as ordinary chat interfaces.
| Trusted layer | Why the organization trusts it | What compromise can inherit |
|---|---|---|
| Identity provider | Authenticates users and issues tokens | Access to every connected relying application |
| Browser session | Already passed authentication and MFA | Active access to SaaS, cloud, email, and data |
| Control plane | Authorized to administer infrastructure | Downstream systems, policies, workloads, and credentials |
| Build pipeline | Authorized to produce and deploy software | Production code and customer environments |
| Machine identity | Performs approved automated work | API actions at scale without interactive login |
| AI agent | Acts through delegated tools and user permissions | Data retrieval, workflow execution, and business decisions |
The phrase “Zero Trust” is easily misunderstood. Organizations cannot operate while literally trusting nothing. Every system eventually needs to authorize an identity, accept a piece of software, rely on a cryptographic key, or permit one service to communicate with another.
The objective is not to eliminate trust. It is to prevent trust from becoming permanent, invisible, unlimited, or transferable without verification.
Under a mature model, trust should be:
CISA organizes Zero Trust around five pillars: identity, devices, networks, applications and workloads, and data. Three cross-cutting capabilities—visibility and analytics, automation and orchestration, and governance—connect those pillars.
This is important because Zero Trust cannot be reduced to one identity product, network gateway, or vendor platform. A user may be strongly authenticated while using a compromised device. A healthy device may access an overprivileged application. A well-segmented application may expose data through an unmanaged API. Strong controls in one pillar do not erase implicit trust in another.
Security reviews often begin by identifying what an organization trusts.
A better question is:
What becomes possible if this trusted system is compromised?
That question changes how risk is prioritized.
A local privilege-escalation vulnerability on an ordinary workstation may expose one endpoint. The same class of vulnerability on an identity provider, management server, code-signing system, or backup platform may expose an enterprise-wide authority.
A service account with no interactive login may appear less important than an administrator. But if it can create cloud resources, retrieve secrets, or deploy production code, its effective authority may be greater.
A low-traffic application may seem unimportant. But if it holds an OAuth grant to read every mailbox or an API key to access customer records, its position in the trust chain changes the calculation.
This is not a mathematical scoring formula. It is a way to expose the risk that traditional vulnerability and asset rankings often miss.
Inventory identity providers, privileged platforms, control planes, software pipelines, signing systems, service accounts, API integrations, browser extensions, and AI tools. Document what each one can authorize downstream.
Use least privilege, just-in-time administration, short-lived credentials, scoped tokens, conditional access, separate administrative identities, and restricted management paths.
Compare identity-provider logs with application access, control-plane commands with endpoint activity, pipeline deployments with code changes, and AI actions with the instructions and data that triggered them.
Ensure teams can invalidate sessions, rotate certificates, disable integrations, revoke OAuth grants, replace machine credentials, remove agent permissions, and isolate management systems without improvising during an incident.
Separate administrative planes from user networks, limit east-west communication, restrict management interfaces, and require resource-specific authorization instead of broad network access.
Ask whether devices remain healthy, identities behave normally, software origins remain valid, service permissions are still necessary, and automation is performing only its intended function.
The end of implicit trust does not mean the end of trusted relationships. It means those relationships must become visible, limited, monitored, and reversible.
Organizations will continue relying on identity providers, cloud platforms, software vendors, management systems, service accounts, and AI agents. The operational benefits are too significant to abandon.
But defenders can no longer treat successful authentication, internal network location, vendor reputation, valid signatures, administrative authority, or model-generated decisions as final proof that an action is safe.
Each is one signal inside a larger decision.
The most damaging attacks increasingly succeed not by defeating trust but by obtaining it. They steal the session, compromise the controller, poison the pipeline, capture the signing key, abuse the integration, or manipulate the agent. Once inside the trusted path, malicious actions inherit the legitimacy of the system performing them.
The security model must therefore change from:
“This system is trusted.”
to:
“This specific action is permitted under these conditions, for this purpose, for this amount of time—and we can verify that it happened as intended.”
Sourcing: Zero Trust definitions and architectural principles are based on NIST Special Publication 800-207 and related NIST guidance. The five Zero Trust pillars and cross-cutting capabilities are based on CISA’s Zero Trust Maturity Model Version 2.0. Forged web cookies and SAML tokens are based on MITRE ATT&CK techniques T1606.001 and T1606.002. Golden SAML was originally documented by CyberArk (2017); the AD FS replication and token-signing certificate extraction technique is based on published Mandiant / Google Cloud threat research. References to AI-agent risk describe a developing security model and should be read as architectural analysis rather than attribution to a single incident.
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