Organizations should start their zero-trust journey with a formal readiness assessment, such as Numberline Security's, which evaluates current identity, access, and security controls to identify gaps before any technology is purchased. This prevents wasted spending on tools that don't fit actual organizational needs.From there, Numberline's four-phase Zero Trust Blueprint (assessment, strategy, roadmap, and execution) offers a structured path forward. The assessment phase establishes a maturity baseline; strategy aligns zero trust goals with business priorities; the roadmap defines phased milestones with timelines and deliverables; and execution rolls out controls like multi-factor authentication, least-privilege access, and network segmentation.Rather than a full overhaul at once, this phased approach lets organizations target high-risk areas first, such as remote access or cloud applications, then expand zero trust principles gradually, grounded in real business value rather than generic best practices alone.

Zero trust is a strategy, not a product. It's a security philosophy and framework built on the principle of "never trust, always verify," guiding how an organization approaches identity, access, and network architecture. No single tool or vendor solution can be purchased to "become" zero trust, because it requires ongoing policy decisions, continuous verification, and cultural change across people, processes, and technology.That said, many vendors market products, like identity management platforms, micro-segmentation tools, and endpoint security solutions, as "zero trust" offerings. These products can support a zero trust strategy by enforcing its principles, but they are implementation tools, not the strategy itself. This distinction matters because organizations that treat zero trust as a product to buy often end up with disconnected tools and no coherent security posture. Successful zero trust adoption starts with strategy: defining protect surfaces, mapping data flows, and setting policies, then selecting technologies that support those goals, rather than the reverse.

There's no fixed timeline for zero trust transformation, since it depends heavily on an organization's size, existing infrastructure, and risk tolerance. That said, most organizations should expect a multi-year journey rather than a quick project. Initial phases, like a readiness assessment and strategy development, often take a few months to establish a maturity baseline and align goals with business priorities.Building out the roadmap and beginning execution, including deploying multi-factor authentication, least-privilege access, and network segmentation, typically spans one to three years for meaningful progress in high-priority areas. Full enterprise-wide maturity, covering identity, devices, networks, applications, and data across all pillars, can take three to five years or longer for large, complex organizations. Because zero trust is a continuous strategy rather than a project with a finish line, many organizations treat it as an ongoing practice. Priorities shift, threats evolve, and new technologies emerge, so the "end state" keeps moving even after major milestones are reached.

The Zero Trust Blueprint is Numberline Security's structured, four phase engagement methodology designed to turn an organization's zero trust intentions into a funded, executable program. Built on real-world experience across dozens of enterprises, it emphasizes rapidly delivering value to the business rather than a long, drawn-out analysis. Each phase feeds directly into the next: Assessment establishes readiness and maturity baselines, Strategy defines vision and program governance, Roadmap aligns access policies with the technical capabilities needed to enforce them, and execution keeps the initiative on track through ongoing checkpoints and outcome- based metrics. Rather than treating zero trust as an abstract goal, the Blueprint gives organizations a repeatable, expert-led path from initial assessment to sustained execution, ensuring investments and decisions stay grounded in measurable business outcomes at every stage.

Yes, enterprises can use the CISA Zero Trust Maturity Model, even though it was originally written for U.S. federal agencies. Because the model is vendor neutral and organized around universal pillars, Identity, Devices, Networks, Applications and Workloads, and Data, its structure applies just as well to private sector organizations working toward zero trust. Many enterprises adopt the ZTMM as a benchmarking tool, using its four maturity stages (Traditional, Initial, Advanced, and Optimal) to assess where they currently stand and set realistic improvement targets across each pillar. Using CISA's model gives enterprises a credible, well documented framework instead of building an assessment approach from scratch, while still allowing flexibility to prioritize pillars based on their own risk profile and business priorities.Organizations that want to go further can use the Numberline Security ZTMM+, which builds on CISA's model with to turn maturity scores into a prioritized roadmap.

ZTMM+ is Numberline's own extension of CISA's Zero Trust Maturity Model, which is widely adopted, covering five pillars: Identity, Devices, Networks, Applications and Workloads, and Data. ZTMM+ is positioned as a more rigorous, practitioner-tested version of that same framework, built from Numberline's own assessment experience rather than a competing model. Five specific enhancements over the CISA baseline: Defines the undefined. CISA's baseline never actually defines its 40 functions across the pillars, so ZTMM+ fills that gap with clear definitions.Clarifies maturity levels. Several functions' maturity progressions were off-target for private-sector environments, so ZTMM+ redefines fifteen of them (the page shows this concretely with an Access Management example, comparing CISA's four-stage wording against ZTMM+'s tightened version). Adds new functions, including Device Lifecycle Management and Secure Internet Access, addressing gaps in the original model.Adds scored attributes. Over 80 characteristics are captured as specific prompt questions, each with four pre-written, level-mapped answers, for a more objective assessment.Adds a methodology, a repeatable process for running maturity assessments and evaluating progress consistently across the enterprise.

An AI agent is a system that uses an AI model to take actions toward a goal, rather than simply producing a response for a person to read and act on themselves. A standard chat assistant answers a question and stops there. An agent can plan a sequence of steps, call tools or APIs, read and write files, browse the web, or run commands, then use the results of those actions to decide what to do next, often with little or no human approval at each step. The defining shift is from output to execution. The chat assistant's mistake is a wrong answer. An agent's mistake can be a wrong action: a file changed, a command run, a message sent, or code committed. That difference is why agents introduce security considerations that traditional AI assistants don't, and why the rest of this taxonomy sorts of agent types by what they're able to touch and how directly they can act on it.

An AI assistant is a tool designed to help a user complete task through direct interaction, typically responding to prompts, answering questions, or performing specific actions when asked. It generally operates within a single conversation or session, waiting for user input before taking the next step, and its scope is often limited to well defined capabilities like scheduling, search, or drafting text. An AI agent, by contrast, is built to pursue a goal with a greater degree of autonomy. Rather than simply responding to one request at a time, an agent can break a task into steps, make decisions about how to proceed, use tools or call other systems, and adjust its approach based on results, often without needing approval at every step. The core distinction is autonomy and initiative. Assistants are largely reactive, waiting on user direction, while agents are proactive, capable of planning and executing multi-step workflows on their own. In practice, the line can blur, since many modern AI assistants now include agentic features, and many agents still rely on user defined goals to start.

Traditional apps run on logic written in advance and only do what they were specifically coded to do. AI agents use a model to reason about a goal and figure out their own path to it, deciding which steps to take and which tools to use, sometimes in ways the developer didn't explicitly program.

Several desktop chat clients let a user grant the app local filesystem access in a couple of clicks. Once that's on, it's no longer a contained chat assistant; it's effectively become a local-access agent, with a different set of controls required. It's worth knowing this setting exists and explicitly deciding whether to allow it, rather than letting individual users flip it on without realizing what it changes.

No. For chat assistants, the answer is straightforward: at most, the same authority. Whatever the user can do, the assistant can do on their behalf, nothing more. That's a useful baseline, though it won't hold for every category in the taxonomy, and it's a good starting point for thinking about potentially over-privileged access that a user, and therefore the assistant, may already have.

Enterprise-managed accounts give real visibility and control over how people use the platform, including SSO integration, domain capture so anyone signing up with a corporate email lands under enterprise management automatically, role management tied to SCIM provisioning, centralized billing, controlled SaaS tenant integration, audit logs, and regulatory support like GDPR or CCPA. Those controls cost money and effort, but standing them up is table stakes for enterprise security at this point.

Formally procure and enforce use of enterprise accounts on sanctioned AI platforms, block unsanctioned platforms and free-tier accounts on sanctioned ones, apply DLP to catch sensitive data in prompts and uploads, inventory every significant connected SaaS platform and start enforcing access limits, and decide and communicate an explicit policy on desktop-client local filesystem access rather than leaving it to individual users.

Formally procure and enforce use of enterprise accounts on sanctioned AI platforms, inventory access across every significant SaaS platform and start enforcing limits on on-behalf-of agentic access, tune identity monitoring for traffic arriving from the AI vendor's IP ranges under a legitimate user's identity, and treat user education as a primary control rather than a consolation prize, since platform reporting will lag the rate of innovation in this category for the foreseeable future.

Imagine asking a sandboxed agent to build a presentation from company data. The agent, working alone in its sandbox, decides the best way to produce a polished chart is to upload the data to a third-party chart-rendering service. The user gets a nicely formatted chart back, but an outside provider now has a copy of the data, and nobody asked first. That decision happened autonomously, on the vendor's machines, outside the enterprise's network and view, which is the core risk this category introduces.

The actions that reach an enterprise's SaaS platforms arrive from the AI vendor's IP ranges under a legitimate user's identity, not from the user's own device or network. That traffic pattern can defeat device-based and network-based anomaly detection, which is why identity-layer monitoring, such as IdP conditional-access rules and identity-anomaly detection, has to pick up the slack instead.

A user could independently create accounts on prohibited or unknown SaaS systems and hand the agent those credentials. The agent would then be able to access those systems and potentially share corporate data with zero enterprise visibility or control, since the account and the access were never provisioned or reviewed by anyone in the organization.

These agents can be configured to run on schedules and act on channel activity indefinitely. When the person who configured an agent leaves the company, or a channel is archived, nothing about the agent's schedule stops automatically. The question of who owns the agent, its service accounts, and its recurring tasks needs an answer in your offboarding and decommissioning checklists.

When an agent responds to any member of a shared channel and can take proactive action, riding a single user's permissions no longer makes sense, whose permissions would apply, and at what moment? A distinct identity lets the agent be governed, scoped, and audited as its own principal rather than blurring into whoever happened to invoke it.

With a single-user agent, the prompt surface is one person. In a channel, the prompt surface is every member, plus every document, message, and link anyone posts. A malicious participant, or malicious content passed along by a well-meaning one, can attempt to steer an agent that holds broader authority than the person who planted the instruction. This is a classic confused-deputy problem, which is why channel membership hygiene matters as much as agent configuration.

Deliberately decide and communicate whether agents operate on behalf of users or with their own access. Validate channel membership for sensitive functions like HR, Finance, and Legal, since every member is part of the agent's prompt surface. Use multiple service accounts rather than one shared account to preserve least privilege and attribution. Define ownership and offboarding processes for agents and their scheduled tasks before adoption scales further.

Four things: access some or all data within the office suite environment, subject to configuration and the user's own access grants; analyze and make recommendations based on that data; perform specifically directed work tasks; and automate work with some degree of agent autonomy when directed by the user.

No. Both vendors are explicit that their agents act on behalf of the user and retrieve only what the requesting user can already reach. Google states plainly that if a user doesn't have access to a document or email, Gemini won't retrieve it. Microsoft says the same about Copilot.

Users typically have far too much access already. Every over-permissioned SharePoint site, every “anyone with the link” Drive file, and every stale group membership was already technically accessible. What changes is that it becomes instantly navigable in plain English to anyone with a license. The agent is an amplifier of whatever permission hygiene already exists.

Users typically have far too much access already. Every over-permissioned SharePoint site, every “anyone with the link” Drive file, and every stale group membership was already technically accessible. What changes is that it becomes instantly navigable in plain English to anyone with a license. The agent is an amplifier of whatever permission hygiene already exists.

It depends on whether the agent is designed to operate without a user prompt, when no human is necessarily present at execution time. Agents supporting logged-in user questions and workflows are fine running on behalf of the user. Agents handling incoming tickets, scheduled overnight work, or triggered workflows are not. Where a platform runs that kind of autonomous work on behalf of users, treat it as a finding and escalate it. Determining which model applies can take time, because vendor documentation is often vague, silent, or outdated, so you may need to ask your vendor's customer success team or test the behavior yourself. Where a platform offers a choice, make it deliberately rather than inheriting the default. Distinct identity is necessary but not sufficient, so also check that the agent's identity fits the platform's existing permission model.

When an agent acts on behalf of a user, all its activity is logged as, and credited to, that user. You cannot have an audit trail indicating that Jane performed an action without being able to distinguish what Jane actually did from what an AI agent did while operating as Jane. Agents acting on behalf of users produce incorrect audit trails, and infosec cannot be responsible for allowing this. Map the identity model for each platform, verify that your audit trail can distinguish agent action from human action, and if it can't, document that gap in writing.

Often, yes. Agents with their own identity frequently have more authority than the person invoking them, because vendors scope them generously by default, since a tightly scoped agent looks disappointing in a demo or proof of concept. That creates a standing pool of access that anyone able to invoke the agent can draw on, a confused-deputy problem. Agents acting on behalf of users have the same authority as the user but far greater practical reach, and when a highly permissioned user invokes one, it holds far more access than the task requires. Audit default agent scoping on every platform rather than assuming least privilege was the starting point, and narrow agent access to what the task actually requires. Where a platform won't let you scope the agent at all, weigh that against whether the agent should be enabled. Re-evaluate user permissions too, knowing an agent may now exercise them at machine speed and scale.

That question may have no answer at all today, especially where agents were configured by a department lead outside central IT. Where platforms give agents their own identities, those identities need the same joiner-mover-leaver discipline as any other service account, and almost nobody is applying it. Establish clear ownership for every agent and validate it periodically, extend access reviews to cover agent identities and their permissions, and add agents and their scheduled work to your offboarding checklists. Governance maturity varies sharply between vendors. Workday built a separately licensed Agent System of Record that manages agents from registration through retirement, while Atlassian's Rovo arrived with an all-or-nothing toggle. Treat governance maturity as a vendor selection criterion going forward.

Because the output often gets executed. A bad outcome here isn't just a bad answer, it can be a commit that persists in a repository, flows through a CI/CD pipeline, and runs in environments far from where it started. That execution path makes Code / Terminal Agents a supply-chain concern, not just an endpoint one, which is a materially different risk category than a chat assistant producing an incorrect response.

Codex, Claude Code, and Gemini for macOS each default to a different access model, so the same task can carry a different risk profile depending on which tool runs it. Codex uses two dials: a permission profile (read-only / workspace / danger-full-access) plus an approval policy. Network access is off by default in workspace mode. Claude Code uses a safe-command allowlist with writes bounded to the working directory. It defaults to read-only. Gemini for macOS limits access to directories a user explicitly connects. Broader reach depends on macOS-level permissions.

No. Desktop agent apps have put filesystem and shell access in front of far more than engineering: finance analysts reconciling spreadsheets, legal teams reviewing contract folders, marketing managers connecting email and calendar, and product managers organizing a Downloads folder all now have this level of access. Engineering teams usually operate under some existing constraints on developer machines. Finance and legal endpoints often don't have equivalent guardrails, which makes those teams a bigger blind spot in practice.

Manage the operating system permission layer through MDM. On macOS, agents need Screen Recording and Accessibility permissions before they can see or drive anything, and those grants are manageable through MDM privacy preference profiles. That lets an enterprise permit or deny computer use across every AI vendor at once, and it survives product changes, though it is all-or-nothing per user. Vendor controls exist but differ substantially. OpenAI is more conservative, with Windows Computer Use and remote control disabled by default for Enterprise, managed configuration, and per-application access control on Windows. Anthropic offers coarser controls through MDM policies for Claude Desktop and an organization-level toggle for Claude in Chrome, with permissive defaults and no documented way to permit file access while denying screen and input control. Know precisely what your platform offers, and standardize on one where you can.

Largely no, because they've already done their work before the agent starts. These agents have no separate identity. They inherit identity by operating a machine where the user is already authenticated: every session, every cookie, every signed-in application. There is no authentication event to observe, because none occurs. Conditional access already passed, and MFA already happened or will be satisfied by the user mid-stream. Your leverage is at the session layer, not the login. Consider shorter session lifetimes and step-up authentication for sensitive applications, since these force a checkpoint the agent cannot pass on its own, and review which applications stay authenticated on a typical endpoint and for how long, since that inventory is effectively the agent's reach.

No. Confirmation prompts assume an attentive human, but the value of these agents is that the human goes and does something else while the agent works. Sessions start with a user prompt, then can run multi-step, long-running work with limited supervision, and both major vendors support scheduled tasks. OpenAI also supports remote control, separating the operator from the screen entirely. A prompt that appears on a screen nobody is looking at is not a control. The stakes are real because UI actions are frequently not reversible and not logged: a misplaced click in an admin console or a record deleted through an interface with no audit trail. Scope by application wherever supported and keep the always-allowed list short and reviewed, decide on remote control before someone enables it, inventory scheduled tasks, and treat agent access to applications with irreversible or unlogged actions as a decision requiring justification.

Everything on the screen. Technically the agent reaches exactly what the user reaches, but in practice it sees more than the user intended to share: the unrelated window behind the document, a personal notification that arrives mid-task, a forgotten browser tab. Anthropic's own documentation warns this can include information belonging to other people. Screenshots also leave the endpoint, since this is the one local-access sub-type where the primary data flow to the vendor is images of your screen. Extend data classification to the endpoint, teach users that closing unrelated windows before a task is a real control, and use per-app scoping where it exists to limit the agent to the applications a task requires.

Assume some exist. An employee can build an agent on a personal Zapier or Make account, connect it to corporate Slack or Google Workspace, and run it on a schedule without touching anything your organization administers. The platform is working as designed; your enterprise simply has no relationship with that deployment. Good starting points are SaaS discovery tooling, OAuth grant reviews in your identity provider, and expense reports. Once you find them, record each agent in a central registry: owner, purpose, identity, data sources, tools, autonomy level and risk tier. And include an re-authorization date with an automated reminder.

Maker-provided credentials. When an agent uses its builder's credentials no matter who invokes it, every user indirectly exercises the maker's privileges. That creates a confused-deputy path to oversharing. The safer options are user context, where the agent acts as the invoking user and existing permissions apply, or a distinct agent identity that you provision, review and retire like any privileged account. Find out which model each platform uses. If nobody chooses deliberately, the platform default decides for you. Also keep in mind that outside your own identity provider's ecosystem, agent governance lives in each vendor's console, so you'll be running parallel governance planes.

Authority on these platforms accumulates through connectors, actions and sharing settings rather than being designed. Common failures include a read-only agent quietly gaining write or delete access, a broad web-access action bypassing your approved connector catalog, and a narrow use case running under an identity that can administer the whole tenant. Strip write, delete, export, financial and admin functions by default. Use an approved connector catalog and scope permissions tightly. Require human approval for irreversible or externally visible actions, set step and spend limits, and keep a kill switch. Start agents in read-only or recommend-only mode and expand only when testing shows the need.

No. These agents let domain experts automate work that would never reach an engineering backlog, and blocking them tends to push activity onto unmanaged accounts. The better model is decentralized creation inside centralized guardrails. Use managed environments, curated