Security automation examples

Check endpoint detections, look for beaconing in network logs, and investigate suspicious sign-ins. Each prompt helps you choose the schedule, evidence sources, and escalation criteria.

Bring SIEM and endpoint alerts into one case, add threat intelligence, and estimate the affected assets. Route the evidence using your team's incident criteria.

An AI agent can validate incoming reports and classify cases from the evidence. Workflows route the case to its owner, page on-call, and track the criteria for escalation or recovery.

Query Splunk, CrowdStrike, and Okta to build a source-linked incident timeline. Have an AI agent propose root cause hypotheses, then assess scope and preserve the investigation record.

Draft regulator and customer notices from confirmed case facts, notify stakeholders by severity, and prepare indicators for sharing. Keep external drafts with the people authorized to approve them.

From a confirmed incident, coordinate endpoint isolation, session revocation, and indicator blocking. Record approvals and results on the case, with rollback steps for blocks that disrupt legitimate activity.

Compare endpoint, cloud, and software inventories to find what each tool misses. Record unmanaged assets and missing owners, then send the gaps to the team that can fix them.

Match threat reports to your software inventory with an AI agent. Prioritize vulnerability findings using asset criticality and exploitation evidence, and track exceptions with expiry dates.

Have an AI agent read case records and propose updates to your incident response plan. Turn concrete fixes from incidents and exercises into tickets with owners.

Collect privileged access grants for owners to review, find accounts that outlive their purpose, and investigate risky sign-ins. AI agents can also draft tighter cloud policies as pull requests.

Follow up phishing simulations while the event is still fresh. Track required training by role, remind people about unfinished modules, and give owners a record of completion.

Find encryption gaps, collect context for data-loss alerts, and follow up failed backup jobs. Record findings with the affected resource and an owner so each gap has a next step.

Compare systems against approved baselines and track overdue patches. Find silent log sources and use an AI agent to sort software findings into risks, policy violations, and review requests.

Compare firewall rules with documented trust boundaries and look for single points of failure. Collect capacity trends and provider evidence for infrastructure reviews.

Turn the recovery plan into assigned tasks. Verify backups before restoring, check systems afterward, and collect the evidence needed to sign off on a return to normal operations.

Build status updates from recovery progress rather than chasing each team for a summary. AI agents can draft customer and public messages from approved facts for communications and legal review.

Connect customer security promises to the controls behind them. Keep obligations, owners, and critical service dependencies in records your team can review when something changes.

Keep risk scores, owners, and review dates in one register. Route exceptions for a documented decision and prepare digests for the people accountable for each risk.

Use case assignments, on-call schedules, and admin roles to draft a security responsibility map. Check for gaps when people join, change roles, or leave.

Compare written policies with settings in Okta and GitHub, chase outstanding acknowledgments, and prepare policy reviews with a record of what changed.

Pull incident trends, failed controls, and overdue findings into leadership reports. An AI agent can assemble the evidence and draft commentary for the next strategy review.

Give an AI agent the sources and requirements for a first-pass vendor assessment. Track supplier risks and access changes from onboarding through offboarding.