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Prevent AI agents from accessing sensitive company data by giving each agent its own identity, allowing only the data and tools one task needs, and enforcing that limit in a deterministic system outside the model. A system prompt or a model guardrail is not the access-control boundary. NIST says so in its Design Tenets for Agent Identity Management Systems: "Guardrails alone are insufficient."
Hold the boundary with short-lived, revocable credentials, a fresh allow-or-deny check on each action, human review for high-impact steps, monitoring aimed at that agent, and audit records the agent cannot change. An instruction hidden in a document the agent reads can try to widen the task. The external check is what keeps the wider request from reaching company data.
Before you rewrite a prompt, gather three things: an inventory of every path to data, a distinct identity for the agent, and a component outside the model that can allow or deny each action. The steps and design choices below are principles for your own architecture. They are not a single product’s settings page, and they are not a promise that every attack fails.
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Start with a path inventory. List every data source, tool, API, memory, and outbound communication path the agent can use, and classify the sensitive data each path can expose. Until that list exists, least privilege has nothing solid to attach to.
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Map reach, including paths you did not mean to open. A ticket search, a file share, a database query, a retrieval index, a browser tool, a code interpreter, a memory store, a webhook, and an outbound email send are different paths when they reveal or move different data. For each one, record the data it can expose, the operations it can perform, and whether the content on that path was written by someone other than the person who assigned the task.
Give memories their own rows. A memory written during one task becomes input on a later task. It can hold a copy of a restricted record, or a paragraph that tries to steer the next run. Give outbound paths their own rows for the matching reason: they are how restricted data leaves, even when the original request looked like a summary.
Classify the path, not the agent as a whole. One workload often touches ordinary text and restricted records. A single label hides the path that needs the tightest scope, the shortest credential, and a person in the loop. That classification is what later decides which calls are denied by default and which actions are worth a human review.
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- A way to authenticate this agent, or this agent role, as its own identity.
- A deterministic check you can place on each sensitive path, returning deny when no explicit allow matches.
- A way to mint a credential when a task starts and revoke it when the task ends, is cancelled, or looks hostile.
- A record store the agent cannot modify.
Keep sensitive paths disconnected until the missing capability exists. The exact policy still depends on how the agent is built, which data classes you use, which tools you connect, and the business impact of a wrong read or a wrong send.
How do I put these controls in place, in order?
Do the work in this order. Later steps assume the earlier ones. Revisit the sequence whenever the agent gains a tool, a data source, a memory, or an outbound destination.
- Write the inventory and classify each row. One row per data source, tool, API, memory, and outbound path, with the data it can expose, the operations it supports, and whether users or outside writers supply the content. You are done when a general-purpose capability such as code execution or an open network call is either on the list with a classification, or removed from the agent.
- Create the identity. Authenticate the workload as that agent or as that agent role. Keep human administrator credentials and broad service accounts off this workload. You are done when a log can name the agent as the account that touched the data.
- Place a deterministic allow-or-deny check on every sensitive row. Scope an allow to the user, the task, the resource, and the action where the platform can express them. Where it cannot, connect a smaller data set or a single-purpose tool until the effective permission matches the task. No matching allow means deny.
- Issue a short-lived credential for the task and keep reusable secrets out of the model context. Revoke the credential when the task ends, when it is cancelled, or when you suspect the agent is being steered. You are done when an idle agent holds no credential and the model never receives a secret it could repeat.
- Separate untrusted content from permissions. Treat retrieved documents, uploads, tool results, and memory read-back as data. Filtering and model-side instruction handling can sit in front of the model. The permission itself stays in the external check, so a paragraph cannot widen it.
- Require a person for defined high-impact actions. Show the reviewer the identity, the delegated intent, the resource, the action, and the data that would move. Leave routine, in-policy actions off that queue.
- Monitor that identity and store independent evidence. Record identity, delegated intent, the allow-or-deny decision, and the resulting action in append-only or tamper-evident storage the agent cannot edit.
- Test the boundary, then re-test on change. Exercise indirect prompt injection, access outside the task, unauthorized tool use, and attempted transmission of restricted data. Repeat after permissions, data sources, tools, or workflows change.
A company that skips the inventory and starts at the prompt has a model that can still call whatever tools the deployment already installed. The prompt is step-level guidance to the model. The boundary is steps 3 and 4.
Why does each AI agent need its own identity?
Treat the agent as a security principal with its own permissions. Give each agent, or each distinct agent role, an identity, and give that identity only the permissions its assigned work requires.
NIST’s agent-identity tenets recommend zero standing privilege and separation of duties. Zero standing privilege means the agent holds no persistent rights while it is idle. Least privilege means the rights issued for a task are only the rights that task requires. Separation of duties means the party that proposes an action is not the authority that already holds broad administrative power, and is not the party that approves the sensitive action. Those three properties need a name they can attach to: the agent’s identity. The Design Tenets for Agent Identity Management Systems frame that identity as something you manage on its own.
Reusing a human administrator’s credentials collapses the design. The agent inherits rights the task does not need. Content that steers the agent inherits those rights as well. The log names the administrator. Withdrawing the agent means withdrawing the person, which is why the shared credential tends to stay. A broad service account taken as a shortcut fails the same way: the account was created to reach many systems, and the agent receives the set.
Size the identity to a role you can describe in one sentence, such as reading one customer’s open tickets. Attach permissions to that sentence. An identity shared by several workflows is a role identity only when those workflows need the same minimum rights. A role that quietly accumulates every team’s tools is a broad service account under a new name.
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Why can’t a system prompt keep sensitive data away from an agent?
Put the allow-or-deny decision in a deterministic system outside the model. Guidance inside the model, including a system prompt, a safety filter, or a guardrail the model interprets, shares the channel the model uses for everything else it reads.
NIST’s design tenets state the limit directly: "Guardrails alone are insufficient." The same article explains the mechanism. Models process instructions and data in a shared stream, so policy separation has to be enforced architecturally. A system prompt, the user’s request, a retrieved paragraph, and a tool result all arrive as content the model handles together. A sentence in a knowledge-base article is in a position to influence the model in the same stream as a sentence in the system prompt.
Policy separation means an external enforcement layer decides what the agent is allowed to do, independently of what the model proposes. The layer needs an identity, a task, a resource, an action, and a rule. It does not need to "understand" the document. If the model asks for a broader export because a paragraph told it to, the rule still returns its own answer.
Filtering and model-side instruction handling remain useful. They cut down how often the model attempts a bad action, and they can flag instruction-like text in retrieved content. They complement the external check. The NIST sentence is an architectural requirement, not a guarantee that any one control stops every attack. Quote it from the design tenets, and build the boundary beside the model.
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How does indirect prompt injection expose company data?
Indirect prompt injection is a malicious instruction embedded in content the agent processes, rather than an instruction typed by the person using the agent. NIST describes it as a route to compromising privacy, and describes tool-using agents being hijacked to execute code or exfiltrate data.
Agents are built to read, so the instruction has many carriers: a customer email, a web page, a ticket, a retrieved file, or a record a tool returns. The person who started the task may have asked for a summary. The embedded instruction asks for a broader set of records, a tool the task does not need, or a copy sent somewhere else. The March 2025 report Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025) places this in the adversarial-ML taxonomy. The operating consequence is to treat external and user-supplied content as untrusted input.
Include retrieved documents in that treatment, including documents stored inside the company. The failure case to exercise is a malicious instruction sitting in a document the agent was asked to read. Because instructions and data share one stream, that document can influence the model the way other text can. Company authorship does not move the paragraph onto a protected channel.
For an agent connected to company data, four entries in the OWASP 2025 Top 10 for LLMs and generative AI applications meet in one incident shape. Prompt injection supplies the steering. Excessive agency supplies tools and permissions broader than the task, so the steered agent has something powerful to do. Sensitive information disclosure is the moment restricted data appears in a reply or a tool call. Vector and embedding weaknesses add a retrieval route: an index can retain sensitive text and can return a chunk that carries the hostile instruction.
| Risk in the OWASP 2025 Top 10 | How it shows up against company data | Control that addresses it |
|---|---|---|
| Prompt injection | Instructions hidden in content steer the agent away from the assigned task | Handle that content as untrusted, and enforce permissions in a layer the document cannot rewrite |
| Sensitive information disclosure | Restricted data appears in a response or in arguments to a tool | Scope the resource and the action, constrain outbound sends, and monitor what left |
| Excessive agency | Installed tools and rights go beyond the task, so a steered model can act widely | Distinct identity, least privilege, zero standing privilege, and deny by default |
| Vector and embedding weaknesses | A retrieval index keeps sensitive text and can surface hostile instructions | Inventory the index, limit which collections a task may query, and treat returned chunks as untrusted |
The chain is specific. Untrusted retrieved content influences the model. The agent can reach more data than the task requires. Retrieved data then leaves in a response or another tool call. Break the chain at the tool and the data boundary. None of the controls in the table is a claim that the matching attack becomes impossible in every design. They are the points where a deterministic rule can refuse the action the model was steered into.
How do I grant an agent only the access one task needs?
Scope every grant to the user, the task, the resource, and the action, and deny the request when no explicit authorization exists. Least privilege is that rule sized to one piece of work. Zero standing privilege adds time: when the task is finished, or while the agent is idle, the rights are gone.
Recheck access as actions occur. A decision that was valid for the first tool call is made again for the next call, because the resource or the action may have changed. An unexpired credential is proof of a current decision for this call. It is not a standing pass for whatever the model asks next.
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| Scope | Bind the grant to | What an unbound scope allows |
|---|---|---|
| User | The person or service this task is for | The agent opens data that belongs to someone else’s session |
| Task | One assigned piece of work | Rights from an earlier task are still live when new content arrives |
| Resource | The specific store, record set, or collection | A useful connector becomes a path into unrelated systems |
| Action | Read, write, delete, send, or execute, as the task requires | A lookup task can still modify records or transmit them |
Where the platform cannot express one of these scopes, narrow the connected data or the tool until the effective permission matches the task. A smaller export, a single collection, or a single-purpose tool is a valid way to reach least privilege when the policy language is coarse. Leave the resource disconnected when you cannot make the effective permission match.
Deny by default is what makes the table operational. An empty policy means no access. A data source or tool discovered after go-live stays unreachable until someone adds an explicit allow for a defined task. That default also keeps the inventory honest: a path found later is denied until it is classified, rather than quietly usable because the agent is already deployed.
How should I issue and revoke an agent’s credentials?
Issue a credential when a task needs it, keep it short-lived and revocable, and keep reusable secrets out of the model context. Prefer task-initiated ephemeral access over long-lived bearer tokens and static secrets that can be presented again later.
| Task-initiated ephemeral credential | Long-lived bearer token or static secret | |
|---|---|---|
| Lifetime | Exists for the authorized task | Remains valid while the agent is idle, until expiry or rotation |
| Reach if the agent is steered | The current task’s scope | Every system that secret can open, until someone rotates it |
| Revocation | Withdraw it when the task ends or the incident starts | Depends on finding and rotating a secret that may already have been in context |
| Zero standing privilege | An idle agent holds nothing | An idle agent still carries that secret’s rights |
| Model context | The reusable secret stays with the issuer; the model asks for an action | A secret that enters context can be repeated in a reply or a tool argument |
Context is part of the shared stream. Content that steers the agent can ask it to repeat what it can see, including a key pasted into a prompt or returned by a tool. Put the reusable secret in a component that releases a task-scoped credential only after the policy check allows the action. The agent requests the action. The issuer and the policy layer decide whether a credential exists.
Build immediate revocation into the same lifecycle. Cancel the task, retire the workflow, or answer a suspected hijack by withdrawing the credential. Any reusable secret that did enter the model context should be withdrawn and replaced. It has become data the agent can be asked to reveal, and it remains a key to whatever systems it opens. Expiry limits the window. Expiry does not notice that the second action is a send rather than a read, or a different resource. Make the authorization decision per action.
Where should the allow-or-deny decision live?
Put a deterministic authorization layer between the agent and each protected resource. The model may propose an action. The decision about permission belongs to the layer outside it.
| Model-side guidance | External deterministic check | |
|---|---|---|
| What decides | The model, reading instructions and data in one stream | A separate component applying an explicit rule |
| When retrieved text conflicts with the rule | Both sit in the same stream the model interprets | The call is allowed or denied by the rule |
| Place in the design | Filtering and instruction handling that complement access control | The access-control boundary |
| Limit | Guardrails alone are insufficient, as NIST’s tenets state | It does not, by itself, explain why the model tried the call; monitoring records that |
Cover every row of the inventory: reads, writes, memory operations, tool calls, code execution, and outbound sends. A policy on the document store that leaves a general-purpose tool untouched does not cover the agent. NIST AI 100-2e2025 notes that tool-using agents may be hijacked to execute code or exfiltrate data, so a command runner or a network-capable tool is a data path.
Check placement structurally. For each inventory row, confirm the call cannot run unless the layer returns allow. If the agent, or a tool it can call, can open the resource on its own, that path is still governed by whatever the model attempts. Bring the path behind the layer, or remove the path.
Which agent actions should require a person to approve them?
Require human approval for sensitive or high-impact actions you define from your data classification and business impact. Show the reviewer the identity, the delegated intent, the resource, the action, and the data that would move. The agent does not approve itself.
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Two designs show the split. They are patterns for applying the controls, not reports of a particular deployment.
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A support lookup. The task is to read one customer’s open tickets and draft a reply to the employee who asked. The identity is a support-agent role. The resource is that customer’s tickets. The action policy allows is read. The outbound path is the reply to the employee. The credential is created when the employee starts the lookup and revoked when the draft is returned. The ticket body is untrusted, because a customer can write instructions there. A ticket that says to list every customer and post the list to an external address is asking for a broader resource and a different outbound path. Policy denies both. Those denials do not depend on a reviewer clicking through a queue of normal lookups. If every ordinary read required a click, the malicious ticket would eventually get the same click.
A repository assistant that can run commands. Code execution is on the hijack path NIST describes. A command runner can reach credentials, source, and network destinations the task text never named. Keep production data and build secrets with the credential issuer. Require a person to approve command execution, or to approve any grant that would let execution reach sensitive systems. The approval view shows the destination and the data the command could read. A bare prompt of the form "Allow this agent?" is how fatigue starts, because the reviewer has nothing concrete to judge.
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Human review sits on top of deny-by-default. It is the extra gate for actions you are willing to permit after a person looks. It is a weak gate for actions policy should refuse outright, such as an external send of restricted data that no approved task requires. A review button on a forbidden action trains people to route around the boundary. Separation of duties still holds: the human reviewer is a different authority from the agent that proposed the call.
What records should I keep when an agent touches company data?
Monitor activity for that agent, and store an independent record of the identity, the delegated intent, the access decision, and the resulting action. Make the record append-only or otherwise tamper-evident, and keep it where the agent cannot modify it.
Each field answers a different question. Identity answers which agent or role acted. Delegated intent answers what task it was authorized to perform. The access decision answers what the policy layer allowed or denied, which can differ from what the model requested. The resulting action answers which tool ran, which resource was touched, and whether anything was sent outward.
Agent-specific monitoring is a separate requirement from a general application log. A log that shows only a shared service account, or only the human who owns the workspace, cannot show this agent’s pattern: repeated denials, calls to tools outside the task, or an outbound attempt that follows a read of restricted records. Those patterns are how a too-wide scope or a bypassed path becomes visible before the next document arrives.
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How do I test whether an agent can still reach restricted data?
Test at the data boundary and the tool boundary. Include prompt injection through retrieved or externally supplied content, unauthorized tool use, and attempts to transmit restricted data. Run the tests again whenever permissions, data sources, tools, or agent workflows change.
Use four failure cases as the minimum set for a sensitive path:
- A retrieved document contains an instruction to leave the assigned task and reach additional data.
- The agent is pointed, by that document or by the user, at a resource outside the task’s scope.
- The agent attempts to call a tool the policy for this task does not allow.
- The agent attempts to send restricted data on an outbound path.
Judge the run by the external layer and by where data moved. Treat the control as having held for that case when the unauthorized tool call is denied even though the model tried to make it, and when restricted fields stay out of the reply, later tool arguments, memory writes, and outbound payloads. Also confirm that a credential for the task is absent before the task starts and absent after it ends. A polite refusal written by the model, with the tool call still succeeding underneath, is a failed boundary.
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NIST AI 100-2e2025 identifies AgentDojo as an evaluation framework for prompt-injection vulnerabilities, and it notes that agent-specific research remains early. A published framework is one way to generate hostile inputs. It does not certify that your paths are closed. Your tools, your data, and your grants will differ from any public case set. Keep the four failure cases pointed at your inventory. Treat the early state of agent-specific research as a reason to re-test when the workflow changes.
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How do I tell that an agent’s data boundary is already failing?
Read the independent log for mismatches between the task you delegated and the action that ran. The patterns below follow from a missing identity, standing privilege, a secret in context, or a check that does not cover the path. They are diagnostic signs, not a census of incidents.
| What you see | What it points to | What to change |
|---|---|---|
| Data access is recorded under a person’s administrator account | The agent has no identity of its own | Issue an agent or role identity and take the person’s credential off the workload |
| The agent can still query a system after the task ends | Standing privilege | Switch to a credential that is minted for the task and revoked when it ends |
| A key or token appears in a reply or a tool argument | A reusable secret entered the model context | Withdraw and replace that secret, and keep the replacement with the issuer |
| A document read is followed by a tool the task does not include | The model followed embedded instructions, and the check is missing or too wide | Deny that tool for this task at the external layer, and re-test with a hostile document |
| Reviewers approve long runs of similar requests | The threshold is low enough to cause approval fatigue | Reserve human approval for defined high-impact actions and let policy handle the rest |
| An outbound call carries fields from a restricted record | A send path is open for data the task can read | Deny that transmission, or require review with the payload visible, and confirm the record is in scope |
When one of these appears on a sensitive path, revoke the credential first. Replace any reusable secret that entered the model context. Read the tamper-evident record of what that identity did: intent, decisions, and actions. Shrink the scope to the task you can still justify. Re-run the four failure cases before the agent is allowed to call the resource again. Reconnecting on the same path, with the same tool, leaves the next hostile document the same opening.
Which control pattern fits which agent?
Use ephemeral credentials, an external allow-or-deny check, default deny, and independent logs whenever any inventoried path can expose sensitive data or send data outward. Consider a looser pattern only where the inventory shows that the path cannot expose sensitive data and has no meaningful way to send data out.
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|---|---|---|
| Permission scope | Per user, task, resource, and action, with deny by default | The platform is coarse, and you have already shrunk the connected data so the coarse grant is still task-sized |
| Credential lifecycle | Task-initiated, expiring, revocable, with the reusable secret held by the issuer | The path cannot expose sensitive data and cannot send data out, and you can still revoke immediately |
| Enforcement | A deterministic policy check outside the model on every path | Never as a replacement. Model-side filters may sit in front of the check; they do not become it |
| Data-flow boundary | Retrieved content, memory, tools, and outbound calls constrained separately from permissions | Nothing in the shared context is sensitive, and nothing in it is treated as a new permission |
| Oversight | Human review at a high-impact threshold, with the action and the data visible | Policy already denies the high-impact actions, and the remaining actions are low impact enough that a review queue would only train reflex approvals |
| Evidence | Agent-specific, append-only or tamper-evident, outside the agent’s control | A general application log may supplement that record. It does not replace a record the agent cannot alter |
| Validation | Indirect injection via retrieved content, unauthorized tools, exfiltration attempts, repeated after change | A prompt an operator types checks direct injection only. It misses instructions hidden in a document or a tool result |
Compare implementations on those rows: how fine the scope is, whether credentials die with the task, whether the allow-or-deny rule sits outside the model, how retrieved content and outbound calls are constrained, whether oversight and evidence are independent of the agent, and whether tests include the indirect paths. A design that scores well on a system prompt and poorly on scope, revocation, and evidence still leaves sensitive data at the tool boundary.
How do traditional security principles change for AI agents?
They stay in force, and they have to be adapted to a principal that reads untrusted content and then acts. NIST’s Summary Analysis of Responses to the Request for Information Regarding Security Considerations for AI Agents, published May 18, 2026, reports that respondents broadly agreed: traditional cybersecurity principles remain relevant and still require adaptation for agents.
The adaptation is specific. Identity still has to be distinct, and it may be an agent or an agent role. Least privilege still has to match the work, and it also has to disappear while the agent is idle, because the next invocation may carry new untrusted content. Monitoring still has to support an investigation, and the record has to survive an agent being steered to hide what it did. Input handling still has to assume hostile content, and that content may be the document the user asked the agent to summarize.
Use NIST AI 600-1, the Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, published July 26, 2024, to organize the wider lifecycle: how the agent is designed, what data it is given, how it is operated, and how it is changed. The profile is a cross-sectoral generative-AI risk profile for that broader work. Runtime authorization is one control inside it, not the whole lifecycle. Pair the profile with the March 2025 adversarial-ML taxonomy when you need shared language for attacks, and with the May 2026 summary when you need the public record that existing security principles are the base respondents agreed to adapt. None of these publications inventories your paths for you.
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After the inventory, the identity, the short-lived grant, and the tests, the component that decides access is still the external policy check. The model can recommend, draft, and request. The deterministic layer allows or denies. Guardrails inside the model remain worth running, and NIST’s design tenets hold that they are insufficient on their own. Agent-specific research is early. Match the pattern to the architecture, the classifications, the tools, and the business impact you actually have, and re-test when any of those change.
Frequently Asked Questions
Is a read-only AI agent safe to point at sensitive company files?
A read-only grant still gives the agent access to those files, and disclosure can happen through an answer, a memory write, or a tool call without any write permission on the file. Scope the read to the records the task names and watch that path under the agent’s own identity. Remove outbound tools the task does not need, because a permitted read becomes an export as soon as a send path remains. Read-only reduces what the agent can change; the external check still has to limit what it can open and where the contents can go.
Can several AI agents share a single role identity?
A shared role fits when every workflow under it needs the same minimum permissions, which is the agent-or-role identity pattern. Workflows that need different resources should be different roles, or the shared grant grows to the wider job. Even a correctly shared role needs a delegated-intent record per task so two runs are distinguishable after the fact. A human administrator login or a broad service account is a separate, wider choice and does not become a role identity just because several agents use it.
Does revoking an agent’s credentials delete sensitive data it already stored?
Revocation withdraws the credential and leaves memory, retrieval indexes, and saved transcripts on disk, including restricted text and any instruction those stores absorbed. Classify each of those stores and decide which identity may read them on a later task. A new short-lived credential can still pull yesterday’s copy if the memory path remains allowed. Treat retention of those stores as its own data-lifecycle decision, alongside credential expiry.
Are the NIST AI agent publications a compliance checklist I can pass?
They are design tenets, an adversarial-ML taxonomy, a generative-AI risk profile, and a summary of comments on agent security. They do not award a passing result for a product stack or a finished deployment. Use them to choose properties your controls must have, then map those properties onto your classifications, tools, and impact. A policy that only cites the publications, without a deny decision on each live path, has not installed the boundary they describe.
What does excessive agency mean for an agent that can see company data?
Excessive agency means the agent can act, or can reach data, beyond the task it was given. On a company system it shows up as inventory rows the task statement does not need: an extra tool, a second store, a memory other tasks can read, or an outbound destination. The OWASP 2025 list places that condition next to prompt injection because embedded instructions can use only the agency left installed. Removing the unjustified rows leaves a steered model with fewer ways to touch sensitive data.
