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Use conventional automation for repeatable, policy-bounded checks and expressly authorized low-regret actions. Use AI to help analysts interpret complex or large volumes of evidence, with defined human oversight. Keep people accountable for ambiguous findings and consequential decisions such as high-impact containment, risk acceptance, or exceptions. The right assignment depends on evidence quality, repeatability, potential impact, reversibility, urgency, and approval policy—not on a universal percentage split.
What belongs to automation, AI, and people?
The distinctions matter: rule-based automation follows specified logic; AI can assist with analysis but may produce uncertain results; people bring organizational context and own decisions. CISA’s security operations guidance treats automation as a way to process alerts, events, or threat intelligence according to conditions set by the organization. NIST’s AI Risk Management Framework calls for defined roles and oversight when humans and AI work together.
| Handling mode | Good fit | Guardrails |
|---|---|---|
| Conventional automation | Deterministic checks, deduplication, enrichment, known false-positive rules, routing, and pre-approved low-regret responses. | Set policy conditions; use trustworthy inputs; log actions; limit permissions; and make actions stoppable or reversible where feasible. CISA describes these practices in its security operations automation guide. |
| AI-assisted analyst work | Summarizing evidence, correlating large datasets, drafting recommendations, or helping an analyst navigate security tools. | Show the evidence behind outputs, define human responsibilities, evaluate performance and uncertainty, monitor the system in operation, and make escalation and override practical. See NIST’s AI RMF 1.0 and its AI RMF Core. |
| Human-owned decision | Ambiguous findings, conflicting evidence, high-impact containment, risk acceptance, exceptions, and incident investigation. | Assign a responsible role; record the rationale and approvals; preserve evidence and decision history; and coordinate response through established processes. CISA’s incident and vulnerability response playbooks and the NICE Workforce Framework describe relevant procedures and roles. |
This allocation is a practical synthesis of CISA and NIST guidance, not an official classification standard. Adapt it to your systems, risk tolerance, legal obligations, and operational consequences.
How to decide who handles a finding
Apply these questions to the specific finding and proposed action. A finding may move between handling modes as evidence improves or the stakes change.
#1 Best Overall
- Is the evidence reliable and corroborated? Check whether the source is primary, corroborative, or authoritative for the claim. Asset inventory and results from credentialed vulnerability scans can add useful context in appropriate cases. If asset identity, exposure, or status is uncertain, do not let an automated action treat that uncertainty as confirmation. CISA discusses source quality and context in its automation guidance.
- Is the decision rule stable and repeatable? If analysts consistently reach the same result under clear conditions, encode the rule and validate it. For example, a system may determine that an alert does not apply to the affected platform or that an indicator is already blocked. Those are candidates for automated handling only when the input data and local policy support the decision.
- What is the consequence of being wrong? Consider impact, scope, urgency, and reversibility. Discarding an irrelevant alert is different from isolating a critical system or accepting risk. Only automate a response when the organization has expressly authorized it and the finding meets the policy’s conditions.
- Does interpretation depend on uncertainty or organizational context? AI may help sift through evidence or prepare a recommendation, but the output should be reviewable and challengeable. NIST says organizations should clarify human roles, document oversight, and assess AI systems before deployment and regularly while they operate.
- Who owns the decision and follow-up? Name the role that must review, approve, investigate, or coordinate the response. The NICE Framework describes defensive cybersecurity professionals analyzing data from defense tools to mitigate risk and incident responders investigating, analyzing, and responding to network incidents.
Set a safe boundary for automated actions
Automation does not mean removing the analyst from every workflow. CISA’s guide describes several possible outcomes: discard items that do not meet policy-defined conditions, take an authorized response, or prepare a recommendation or enriched case for analyst review or approval.
- Define conditions before action. Specify which evidence, confidence, asset scope, and policy criteria must be present. Make exceptions and escalation conditions explicit.
- Limit the action to its authorization. Give an automated process only the permissions it needs. A rule allowed to enrich or route a finding should not implicitly gain authority to contain systems.
- Keep a usable record. Log the inputs, rule or model output, action, and any human approval so teams can reconstruct what happened.
- Provide a way to intervene. Where feasible, allow authorized staff to stop or reverse an action and to route uncertain cases for review.
- Review the workflow in operation. Check whether inputs, rules, and outcomes still fit policy and operational conditions; adjust the boundary when they do not.
CISA also notes that manual security workflows are often designed around analyst work. To gain value from automation, organizations may need to redesign processes so systems handle triage and prioritization while people focus on judgment that requires them.
Rank #2
Where AI agents fit—and where they do not
A CISA-hosted NSTAC report describes potential AI and machine-learning uses in cybersecurity, including data triage, monitoring, incident-response support, vulnerability management, and copilots that assist security professionals. These are described as capabilities and examples, not evidence that a particular product will work effectively in every environment.
For an AI-assisted workflow, make the human-AI arrangement operational rather than informal: specify who reviews outputs, what evidence the system must show, how uncertainty is handled, how errors are detected, and when the process escalates. NIST AI RMF 1.0 organizes AI risk work into Govern, Map, Measure, and Manage. It is voluntary guidance, not a universal legal requirement; NIST’s program page says version 1.0 is being revised.
Use the guidance in the right scope
CISA’s incident and vulnerability response playbooks standardize procedures for Federal Civilian Executive Branch agencies. CISA says their broader practices can also be useful to public and private organizations. The vulnerability response playbook is not a replacement for an existing vulnerability management program. The CISA playbook page provides that scope.
NIST published AI RMF 1.0 on January 26, 2023. Its voluntary framework is guidance for managing AI risks, rather than a prescribed staffing or automation model. NIST’s AI Risk Management Framework program page says the framework is being revised and describes a critical-infrastructure profile concept note released April 7, 2026.
Rank #4
There is no universal percentage split
The cited official guidance gives examples, roles, and risk-management practices; it does not prescribe what percentage of findings should go to automation, AI, or people. Set the division based on your evidence, policies, systems, and consequences, then test and review it in operation.
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