Use conventional automation for stable, policy-bounded checks and pre-approved, low-regret actions; use AI to help people interpret complex evidence; keep accountable people responsible for ambiguous, high-impact, or consequential decisions. The right assignment depends on evidence quality, repeatability, impact, reversibility, urgency, and explicit authorization—not on a universal percentage of findings.
What belongs to automation, AI, and people?
The distinction is between applying known rules, assisting with analysis, and owning a decision. These are complementary roles, not competing choices. The allocation below 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.
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| Handling mode | Good fit | Guardrails |
|---|---|---|
| Conventional automation | Deterministic checks, deduplication, enrichment, known false-positive logic, routing, and pre-approved low-regret responses. | Define policy conditions, use trusted inputs, log actions, bound permissions, and provide a way to stop or reverse actions where feasible. |
| AI-assisted analyst work | Summarizing evidence, correlating large data sets, drafting recommendations, or helping an analyst navigate security tools. | Show source evidence, define human roles, evaluate performance and uncertainty, monitor after deployment, and make escalation and override practical. |
| Human-owned decisions | Ambiguous findings, conflicting evidence, high-impact containment, risk acceptance, exceptions, and incident investigation. | Assign a responsible role, document rationale and approvals, preserve evidence and decision history, and coordinate response when needed. |
CISA describes security operations automation as policy-driven processing: a system may discard irrelevant items, take an authorized response, or prepare a recommendation for analyst review. It recommends identifying the information and conditions that let teams handle alerts and events consistently under local policy. For example, a rule might identify an alert that does not apply to the affected platform or an indicator that is already blocked. See CISA’s security operations automation guide.
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How to decide who handles a particular finding
Apply these questions to the finding and its proposed response—not just to the alert category. A low-confidence signal may warrant human investigation even if its type is familiar; a well-understood signal may still require approval if the action could disrupt a critical service.
- Is the evidence trustworthy and corroborated? Check whether the finding is supported by useful sources such as asset inventory or credentialed vulnerability scanner results. CISA distinguishes primary, corroborative, and authoritative information sources; use context that is relevant to the decision rather than relying on alert count alone.
- Is the decision rule stable and repeatable? If analysts consistently apply the same rule, encode and validate it before automating. If the rule depends on changing context or expert interpretation, keep a person in the decision loop.
- What happens if the decision is wrong? Consider impact, scope, urgency, reversibility, and whether local policy expressly authorizes the response. The greater the potential harm or difficulty of undoing an action, the stronger the case for review and approval.
- Would AI help interpret the evidence? AI can assist with summarizing or correlating large amounts of information, but its output needs oversight, evaluation, and a practical way to challenge or escalate it. Treat a generated recommendation as assistance, not as proof or authorization.
- Who owns the final decision and follow-up? Name the role responsible for investigation, approval, or response. Make sure that role can access the evidence and understands what automation or an AI system has already done.
Set the automation boundary before it acts
A rule should specify its inputs, conditions, permitted action, and stopping point. For example, a team might allow an automated response only when a finding meets defined low-regret criteria and the evidence is sufficiently reliable. If the evidence conflicts, the asset is outside the rule’s scope, or the action exceeds the system’s authorization, the workflow should stop and route the case for review.
- Use trusted inputs: identify the sources and data quality needed to make the decision.
- Limit permissions: give a workflow only the access needed for its authorized actions.
- Keep a record: log the evidence considered, rule applied, action taken, and any approval.
- Plan for exceptions: provide a route to pause, escalate, or reverse an action where feasible.
- Review performance: check whether the rule still behaves as intended as systems and policies change.
CISA also cautions that manual processes are often designed around analysts. Organizations may need to redesign workflows so automation can perform triage and prioritization effectively, rather than simply inserting a tool into an unchanged manual process.
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AI and machine learning may support data triage, monitoring, incident response, vulnerability management, and security copilots, as described in the NSTAC report hosted by CISA. These examples describe potential uses, not a guarantee that a product will perform effectively in a particular environment. CISA says it does not endorse commercial products or services on its resources pages.
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NIST’s AI Risk Management Framework 1.0, published January 26, 2023, organizes AI risk work into Govern, Map, Measure, and Manage. It calls for clarifying human roles in human-AI configurations, documenting oversight, and testing AI before deployment and regularly while it operates. In practical terms, define who reviews output, how uncertainty is assessed, when a case must be escalated, and how people can challenge or override a recommendation. NIST describes the framework as voluntary guidance; its program page says it is being revised and notes a critical-infrastructure profile concept note released April 7, 2026.
Keep investigation and accountability with people
People remain essential when findings are uncertain, evidence conflicts, an action has significant consequences, or the decision depends on organizational context. The NICE Workforce Framework describes defensive cybersecurity professionals as analyzing data from defense tools to mitigate risk, and incident responders as investigating, analyzing, and responding to network incidents. Assign those responsibilities through your organization’s incident response and vulnerability management roles and processes.
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. See CISA’s playbooks and cybersecurity guidance and the NICE Framework.
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The official guidance cited here provides examples, roles, and risk-management practices; it does not prescribe what percentage of security findings must go to automation, AI, or people. Set boundaries based on your evidence, policy, operational risk, and ability to monitor and correct the workflow—not an unsupported industry benchmark.
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