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Keep people accountable for consequential network changes. An AI system can summarize telemetry, identify likely causes, and prepare a tightly bounded remediation, but an independent policy layer must decide whether that action is authorized. Require an engineer’s approval for high-impact, hard-to-reverse, or poorly evidenced changes; permit only narrowly defined low-risk actions to run automatically; and stop and escalate whenever a safety check, retry policy, or verification step fails.
This design is more reliable than asking a model to follow a prompt such as “do not make risky changes.” Prompts can guide recommendations, but they cannot enforce permissions. Enforcement belongs in action registries, target protection, authorization, rate limits, risk ceilings, pre-change snapshots, post-change checks, and audit logs.
Decide what requires a human before you automate
There is no universal percentage of network changes that should require approval. Set the boundary with a local risk assessment. The important questions are whether a mistake could affect many devices or customers, whether a dependable rollback exists, whether telemetry and maintenance context are current, and whether the team can recover within its service commitments.
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- Blast radius: Count the devices, services, sites, tenants, and users that could be affected if the action is wrong or repeated.
- Reversibility: A known pre-change state and a tested rollback reduce risk; an irreversible data-plane or firmware operation increases it.
- Evidence quality: Fresh, corroborated telemetry supports safer decisions than stale, missing, or contradictory signals.
- Operating context: Maintenance windows, emergency exceptions, customer commitments, and known incidents can change the acceptable action.
- Recovery capability: Consider out-of-band access, spare capacity, backup configurations, and the time needed to restore service.
NIST’s NCCoE DevSecOps reference model states: “Risk assessments should be performed and used to determine which higher-risk and higher-impact decisions should require a human in the loop.” That is guidance for applying risk assessment, not a universal numerical threshold.
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Separate the AI’s recommendation from execution authority
Use two distinct components. The model is an analyst: it summarizes observations, proposes hypotheses, and drafts an action with an expected effect. The execution service is an enforcer: it checks identity, policy, scope, parameters, rate limits, and current device state before anything runs. The model must never be the final authority over its own permissions.
What the analysis component should produce
- A concise problem statement and the signals supporting it, including timestamps and data freshness.
- Alternative explanations and any contradictory evidence.
- A proposed action from a fixed catalog, not an arbitrary command string.
- The intended target set, parameter values, expected effect, risk classification, and reversibility.
- Prior attempts, their results, and a rollback method based on captured state.
- A confidence or uncertainty description that helps an operator judge the recommendation, without treating a model score as authorization.
What the authority component must decide
- Whether the action type exists in the approved registry.
- Whether every target is allowed and whether any target is protected.
- Whether parameters are inside declared bounds and the change stays below the local risk ceiling.
- Whether the requesting identity, approver, maintenance window, and exception records are valid.
- Whether global and per-target rate limits permit another attempt.
- Whether required telemetry, policy data, and rollback state are available and fresh.
NIST recommends managed identities, continuous authentication and authorization, and least-privilege access for AI components. Treat the model, orchestration service, and device-facing executor as separate principals with separate credentials and logs.
Use explicit approval tiers
The following tiers are a practical policy pattern. They are not a universal standard; map them to your own criticality, blast radius, reversibility, and recovery assessment.
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|---|---|---|---|
| Advisory | Analyzes telemetry and recommends a change. | An operator performs the change manually. | Use when evidence is uncertain, the action is novel, or the target is sensitive. |
| Approval required | Prepares a bounded action and an evidence package. | An authorized operator reviews and approves each execution. | Use for material service impact, multi-device scope, or changes with meaningful rollback risk. |
| Pre-authorized low risk | Runs a catalogued action automatically after independent checks. | Policy grants standing authorization; an operator is notified or can intervene. | Use only for narrow, reversible actions on allow-listed targets below a defined risk ceiling. |
| Blocked and escalated | Stops without making a change and creates an incident or approval request. | A designated human decides whether and how to proceed. | Use for protected resources, unknown actions, failed checks, degraded operation, or high-impact changes. |
Document the owner for each tier, the maximum target count, parameter ranges, approval expiration, and the conditions that revoke standing authorization. A temporary emergency exception should have an explicit end time and an accountable approver.
Create an action registry and protected-target list
An action registry turns vague automation into a finite set of operations. For every action, record its identifier, description, required inputs, parameter limits, expected effect, risk level, rollback procedure, verification test, and permitted approval tier. Version the registry so an audit can show which definition was active when a change ran.
Allow-list actions, not free-form commands
Examples of safer registry entries include clearing a specific interface counter, requesting a bounded diagnostic, or shifting one pre-approved service endpoint to a standby path. A registry entry should reject extra flags, shell metacharacters, unrecognized device capabilities, and values outside its schema. If the model emits a command rather than a registry identifier, parse it into a known action or block it.
Protect resources explicitly
Maintain a protected-target set for core routers, identity and access systems, management planes, safety systems, production databases, and any device under an active change freeze. Protection should be evaluated by the executor, not merely described in a prompt. A target that cannot be classified must be treated as protected until an operator resolves it.
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Put independent guardrails in the execution path
Use defense in depth: prompt constraints can improve recommendations, response parsing can reject malformed output, policy guardrails can evaluate risk, and the final executor can enforce authorization immediately before the device call. No single layer should be trusted to catch every unsafe proposal.
- Validate the action identifier. Confirm that it is present in the current registry version and that the requested operation is enabled in this environment.
- Resolve and validate targets. Convert names to canonical device and interface identifiers, verify ownership and scope, and reject aliases that expand beyond the approved set.
- Check parameters. Enforce type, range, dependency, and cardinality constraints. Do not silently coerce an invalid value.
- Evaluate authorization. Authenticate the calling service, check its managed identity and role, verify the approver when required, and confirm that any emergency exception is active.
- Apply rate and concurrency limits. Check per-device, per-action, tenant, and global budgets, including retries already consumed by other workers.
- Recalculate risk from current state. A previously low-risk action can become high-risk during an outage, convergence event, capacity shortage, or maintenance freeze.
- Require complete safety data. Confirm that pre-change state, rollback data, target health, and required telemetry are available and within their freshness windows.
- Fail closed. If a registry lookup, target check, authorization service, rate-limit store, or policy evaluation errors or times out, block the action and escalate instead of guessing.
The network-governance IETF document describes this separation and specifically warns that communicating constraints to an AI does not replace independent programmatic validation. That document is an Internet-Draft, not a finalized RFC or binding requirement.
Define escalation triggers before deployment
Escalation is a designed operating mode, not a generic “call an engineer” message. The network-specific IETF Internet-Draft gives useful examples of concrete triggers:
- The AI recommends intervention but no pre-authorized action matches the recommendation.
- The remediation loop exhausts its configured retry count without restoring the desired state.
- A global or per-target action-rate limit is exhausted while anomalies remain.
- The proposal exceeds the autonomous risk ceiling or touches a protected target.
- The system detects a network convergence event or another unstable state in which another change could amplify disruption.
- A rollback is triggered, fails, or leaves the device state different from the recorded pre-state.
- The agent is operating in a degraded mode, such as missing telemetry, stale policy data, unavailable tools, or an unhealthy model service.
- Post-change verification disagrees with the expected effect or cannot obtain a trustworthy current state.
Configure these triggers explicitly, with thresholds, timers, and ownership. “High confidence” is not a substitute for a trigger that an executor can evaluate.
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Give the operator a decision-ready escalation
An alert should contain enough context to approve, reject, or request more evidence without reconstructing the incident:
- Affected device, interface, service, tenant, and geographic or logical scope.
- Observed signals, source systems, timestamps, freshness, and conflicting observations.
- Suspected cause, alternatives considered, and the reason intervention is proposed.
- Exact registry action, parameters, expected effect, risk level, reversibility, and policy rule.
- All previous attempts, responses, retry and rate-limit counters, and current health indicators.
- Captured pre-change state, rollback steps, estimated recovery time, and any unavailable dependency.
- The governance constraint that caused the escalation and the deadline before service impact worsens.
Run a consistent human-approval workflow
Approval should be a controlled transaction rather than an informal chat response. The following sequence works for both routine changes and incident response.
- Open the case. Create a unique incident or change identifier and bind every recommendation, approval, execution, and verification record to it.
- Freeze the evidence. Store the input context, telemetry references, timestamps, model version, policy version, and action-registry version used to form the proposal.
- Check scope. Confirm the target list, customer or service impact, maintenance status, protected-resource status, and applicable exception records.
- Review the action. Compare the proposed parameters with the registry, expected effect, alternatives, and known failure modes. Reject proposals that require editing the action into a different operation.
- Confirm recovery. Ensure pre-state capture is complete, rollback is executable through an independent path, and the operator knows how to stop a partial rollout.
- Authenticate approval. Record the approver’s managed identity, role, timestamp, decision, expiration, and any conditions, such as a one-device canary or a ten-minute observation window.
- Execute with a lease. Give the executor a short-lived authorization containing the exact action, targets, parameters, and limits. Do not grant a broad session credential.
- Verify and close or escalate. Query current device state, test the expected service outcome, compare it with the pre-state and policy goal, and either close the case with evidence or escalate immediately.
For multi-device changes, make the approval specify rollout order, batch size, pause criteria, and the condition for automatic rollback. Approval for one batch must not silently authorize later batches after the network state has changed.
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Apply least privilege and zero-trust controls to AI components
Extend zero-trust principles to every AI-related service. Inventory the model endpoint, retrieval service, orchestrator, policy engine, executor, telemetry readers, and device connectors. Give each a managed identity and only the API, infrastructure, and data permissions it needs.
- Use read-only telemetry credentials for analysis services whenever they do not need to write.
- Separate approval identities from execution identities so the executor cannot approve its own request.
- Restrict device credentials by command family, target group, and time window.
- Require continuous authentication and authorization for long-running workflows; do not assume a previously approved session remains safe.
- Rotate secrets, log credential use, and revoke leases when an agent enters degraded mode or an incident changes severity.
- Prevent prompt-injected text, retrieved documents, or device output from changing policy, target scope, or authorization fields.
Capture state, make changes reversible, and verify current reality
Before execution, capture the exact configuration and operational state needed to restore service: relevant routes, interface settings, policy objects, timers, dependencies, and the device software version. Store it immutably with the case identifier and a timestamp. A cached telemetry snapshot is not a rollback plan.
Rollback should be an approved registry action with its own authorization and verification. Define what happens if only part of a batch succeeds, if the device becomes unreachable, or if the rollback itself exceeds a rate limit. Keep an out-of-band recovery path for management-plane failures.
After execution, query the device and service directly. Verify both the technical state (for example, the intended route, policy, or interface condition) and the user-visible objective (such as reachability, error rate, or session recovery). Use current state rather than assuming the pre-execution telemetry is still valid. If verification is unavailable or contradictory, classify the result as unverified and escalate.
Build an audit trail that can explain every decision
NIST guidance calls for traceability to source context, established control gates, accountable approval, and auditability. A useful event record contains:
| Record element | What to retain |
|---|---|
| Context | Telemetry references, timestamps, freshness, incident or change ID, and relevant maintenance or exception state. |
| Recommendation | Model and prompt-policy version, proposed action, targets, parameters, alternatives, uncertainty, and expected effect. |
| Governance decision | Registry and policy versions, risk tier, checks performed, failures, rate-limit status, and why the request was approved, rejected, or escalated. |
| Approval | Approver identity, role, timestamp, scope, conditions, expiration, and decision rationale. |
| Execution | Executor identity, lease, command or API transaction ID, device response, start and finish times, and partial-success details. |
| Outcome | Post-change observations, verification tests, rollback events, final state, and closure or escalation owner. |
Protect logs from alteration and synchronize clocks across the model, policy, orchestration, and device systems. An audit that cannot connect a recommendation to an approval and a verified outcome cannot establish accountability.
Use stricter controls for OT and critical infrastructure
Operational technology and critical infrastructure require fail-safe behavior because a network action can affect physical processes, safety, or public services. A joint guidance announcement summarized by the National Security Agency on December 3, 2025, calls for incorporating a human in the loop for critical decisions, testing and monitoring systems, and implementing fail-safe mechanisms.
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In these environments, include control-system owners and safety personnel in the approval roster, define a safe state for communications loss, and test automation in representative segments before production use. Do not assume that a rollback restores a physical process immediately; the process may have momentum, interlocks, or manual recovery steps that are outside the network executor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare operating models by risk, not by marketing labels
| Operating model | Strength | Weakness | Best fit |
|---|---|---|---|
| Advisory-only | Human retains direct execution control and can add context the system lacks. | Slower response and greater chance that an operator misreads or skips a recommendation. | Novel actions, protected systems, immature telemetry, and high uncertainty. |
| Approval-gated automation | AI prepares repeatable changes while an accountable person reviews evidence and scope. | Approval queues can delay remediation during incidents. | Material but reversible changes with an available on-call approver. |
| Bounded autonomy | Pre-authorized low-risk actions can run quickly and consistently. | A misclassified condition can repeat an allowed action until limits are reached. | Narrow registry actions, allow-listed targets, strong verification, and tested rollback. |
| Automatic block and escalation | Prevents execution when governance data or safety checks are unavailable. | May leave a fault unresolved until a human responds. | Unknown actions, protected targets, degraded agents, failed retries, and exceeded risk ceilings. |
Compare designs using impact and blast radius, reversibility and recovery, the autonomy boundary, uncertainty and context freshness, and the quality of independent enforcement and evidence. A faster model is not safer if it has a wider target scope or weaker verification.
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Stage 1: Inventory and classify
List network assets, services, action types, identities, telemetry sources, and existing change controls. Classify targets by criticality and identify protected resources. Record which actions are reversible and what state is needed to restore them.
Stage 2: Build the registry and policy engine
Encode action schemas, parameter limits, target allow lists, risk tiers, rate limits, approval roles, maintenance rules, and fail-closed behavior. Version policies and registry entries.
Stage 3: Start in advisory mode
Run the model without execution authority. Measure the freshness and completeness of its evidence, the rate of unsupported proposals, and the quality of suggested rollback plans. Have operators label false positives, missing context, and unsafe scope.
Stage 4: Add approval-gated execution
Introduce short-lived execution leases, authenticated approvals, pre-state capture, and post-change verification. Begin with one device class or service and require a canary for larger changes.
Stage 5: Enable bounded autonomy selectively
Promote only actions that have stable telemetry, a tested rollback, clear verification, narrow targets, and an owner who accepts the risk. Set automatic expiry dates for standing authorization and review them after incidents.
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Stage 6: Exercise escalation and recovery
Simulate missing telemetry, policy-service outages, stale rate-limit data, agent degradation, retry exhaustion, convergence events, partial rollouts, and failed rollback. Confirm that each condition blocks safely, alerts the right person, and preserves enough evidence for recovery.
Test the controls and watch the right signals
Do not evaluate the program only by the number of automated changes. Track whether governance works under stress:
- Percentage of proposals mapped to registered actions.
- Percentage of executions with complete pre-state and post-change verification.
- Approval latency by risk tier and the number of expired or rejected approvals.
- Counts of blocked actions by trigger, including protected-target attempts and missing-data failures.
- Retry, rate-limit, rollback, and partial-success events.
- Incidents in which the recorded policy, approver, or target scope was incomplete.
- Time from escalation to an accountable human decision and time to restore service.
Use these measures for control improvement, not as permission to raise autonomy automatically. A low block rate may mean the policy is effective, or that detection and classification are missing failures.
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Common failure modes and fixes
The prompt says “never touch production,” but the agent does
Cause: Prompt text was treated as authorization. Fix: Enforce protected targets and action scope in the executor, and test malformed or adversarial model output.
An approved action expands to more devices than intended
Cause: Aliases, dynamic groups, or stale inventory changed the target set. Fix: Resolve canonical identifiers at execution time, display the final set for approval, and reject scope changes after approval.
The agent repeats a harmful remediation
Cause: Retry and rate limits were local to one worker or were reset after a restart. Fix: Store counters centrally, bind them to the incident and target, and escalate when the budget is exhausted.
Rollback succeeds technically but service remains impaired
Cause: The rollback restored configuration but not the service dependency or physical process. Fix: Verify user-visible outcomes, document dependencies, and include operations or safety owners in recovery plans.
An operator approves with insufficient context
Cause: The alert omitted freshness, alternatives, prior attempts, or expected effect. Fix: Make those fields mandatory and prevent approval while required evidence is unavailable.
What current guidance does—and does not—establish
NIST’s AI Use Taxonomy publication, dated March 26, 2024, describes 16 AI-use activities to help classify how AI contributes to outcomes. The count is a descriptive taxonomy, not evidence that any particular approval design reduces network incidents.
The NIST NCCoE material is a practical DevSecOps reference model rather than a network-specific standard. The detailed network triggers and controls described above come from an IETF Internet-Draft, whose status can change and which is not a finalized RFC. The NSA statement dated December 3, 2025, summarizes joint operational-technology guidance and its stated mitigations. No outcome statistic establishing that human approval or escalation improves AI-powered network operations was identified in these sources, so organizations should validate their own controls through staged testing and incident evidence.
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