What AI brings to incident response
AI-powered incident response applies machine learning and automation to tasks that typically slow down security teams: alert triage, signal correlation, and initial investigation. Instead of analysts piecing together logs and events manually, AI systems analyze activity continuously and highlight patterns that merit attention.
In practice, AI contributes in a few important ways:
Absorbing high-volume signal noise: Cloud environments produce a constant stream of control-plane events, identity activity, configuration changes, and workload behavior. AI helps make sense of this volume without overwhelming analysts.
Spotting meaningful deviations: By learning what regular activity looks like, AI can surface anomalies that may indicate compromise — unusual identity behavior, unexpected access paths, or atypical workload actions.
Assembling context automatically: Effective incident response depends on understanding what happened across multiple systems. AI can correlate related events, identify involved resources and identities, and present a coherent investigation starting point.
Prioritizing based on environment impact: Rather than treating all alerts equally, AI can elevate those tied to sensitive data, internet exposure, or privileged identities.
AI doesn’t replace analysts, nor does it remove the need for human judgment. It reduces the manual effort required to understand what matters, allowing teams to move faster and more consistently without changing their existing response workflows.
Where AI accelerates incident response in practice
AI contributes most in the parts of incident response that traditionally absorb the majority of analyst time – reviewing noisy alerts, assembling context, and determining what actually matters. In cloud environments, these steps become even heavier due to the volume of signals and the complexity of identity and configuration data. AI helps streamline several of these workflows:
1. High-volume alert triage
Cloud environments generate a steady flow of control-plane events, workload telemetry, access logs, and configuration changes. AI can sort through this activity at scale, suppressing clearly benign behavior and surfacing signals that warrant analyst review.
2. Event correlation and investigation
Incidents often span multiple systems – an IAM key used from a new location, followed by unusual API calls, followed by a configuration change. AI can connect these related events automatically, producing an investigation timeline that gives analysts a clear starting point.
3. Contextual prioritization
Not every anomalous event is a threat. AI can weigh factors such as internet exposure, privilege level, recent changes, data access, and network reachability to elevate issues that present actual risk. This reduces time spent on alerts that are unusual but harmless.
4. Pattern and behavior detection
Some threats are subtle — gradual privilege creep, small configuration deviations, or low-and-slow probing. Machine learning models can surface these behaviors earlier by tracking long-term patterns across identities and workloads.
5. Assisting with containment actions
For well-understood scenarios (like access token misuse or known misconfigurations), AI can suggest or initiate containment actions within predefined guardrails. This keeps response times low while maintaining human oversight for high-impact decisions.
6. Post-incident summarization
AI can automatically generate summaries of what occurred, which resources were affected, and what changes contributed to the incident. This helps with documentation, handoffs, and iterative improvement.
AI’s role here is additive – it removes friction from workflows where speed and consistency matter most, while analysts retain control over decisions that require judgment or deep contextual understanding.
How Grammarly’s security team used AI to shrink investigation time by 90%
Grammarly’s security engineering team already maintained a strong cloud defense program, but investigations still required analysts to manually pull context from multiple sources — identity logs, configuration histories, workload metadata, and deployment activity. Even in well-understood scenarios, assembling this picture could take 30–45 minutes before an analyst felt confident about impact and next steps.
To streamline this, Grammarly introduced an AI-assisted investigation workflow that automatically gathers the relevant cloud context the moment a new alert appears. The system reconstructs the sequence of events, correlates identities, resources, and configuration changes, and presents a structured summary that analysts can validate rather than rebuild from scratch. This shifted the team’s time from “hunting for context” to evaluating risk and deciding on the right follow-up actions.
The impact was significant: investigation time dropped by around 90%, with many reviews now taking about four minutes instead of nearly an hour. It also boosted consistency across the SOC — every analyst, regardless of experience level, starts from the same complete, context-rich view of each incident. Rather than replacing human judgment, AI became a force multiplier, allowing the team to maintain speed and rigor as their cloud environment continued to scale.
-> See an example of the AI investigation prompt Grammarly’s team uses to generate consistent, high-quality analysis.
Implementation best practices for introducing AI into incident response
Organizations adopting AI into their incident-response processes often begin with narrow automation pilots, but long-term success depends on designing for trust, consistency, and SOC-wide maturity. Across teams that have successfully operationalized AI-assisted investigations, several strategic patterns consistently emerge.
Start with a narrow, high-confidence workflow
Early implementations work best when focused on the most repetitive, low-risk part of IR—typically the initial context-gathering or triage phase. These workflows are structured, predictable, and easy to validate, which allows teams to demonstrate value quickly while minimizing operational risk.
Use structured prompts and well-defined data inputs
AI outputs are only as reliable as the data and instructions behind them. Successful teams constrain their initial workflows to a small set of authoritative signals—configuration history, identity activity, resource metadata, workload telemetry—and use prompts that produce consistent, review-ready summaries. Clear boundaries prevent unpredictable responses and help analysts build trust in the workflow.
Keep analysts responsible for interpretation and action
AI-generated investigations serve as a first draft, not a replacement for human judgment. Analysts still verify the narrative, assess impact, and decide on the appropriate response. This hybrid approach preserves accountability while eliminating the manual correlation steps that typically slow investigations.
Shift from tactical efficiency to strategic capability
Once the AI workflow is reliable, mature teams don’t simply automate more tasks. Instead, they use the freed-up analyst capacity to strengthen the SOC at a strategic level. By offloading routine investigation steps, analysts can spend more time threat hunting, improving detections, refining playbooks, and addressing systemic weaknesses. The shift isn’t “more automation”; it’s moving the SOC beyond daily firefighting and toward building better systems and long-term resilience.
Establish metrics and continuous feedback loops
Effective programs track investigation time, override rates, model accuracy, and analyst feedback to refine prompts and data inputs. Without this feedback cycle, AI workflows stagnate or drift. With it, the accuracy, consistency, and usefulness of AI-assisted investigations improve over time—and analyst trust grows alongside them.
How Wiz Defend brings AI into cloud incident response
Wiz Defend provides the detection and response layer of the Wiz platform, and the SecOps AI Agent is a core part of that experience. Instead of forcing analysts to piece together signals from multiple tools, Wiz Defend and the AI Agent automatically assemble the context needed to understand and respond to cloud threats.
Automated investigations with the SecOps AI Agent
When Wiz Defend surfaces a new or updated threat, the SecOps AI Agent immediately analyzes it using the Wiz Security Graph.
The Agent produces a review-ready investigation that includes:
what triggered the finding
which identities and resources were involved
the sequence of events
potential blast radius
a confidence score and supporting evidence
This turns what is normally hours of manual correlation into a packaged summary analysts can validate in minutes.
Guided next steps with guardrails
For threat types that have clear, well-understood remediation paths, Wiz Defend can recommend or assist with actions such as isolating a workload, revoking credentials, or rolling back a configuration change. All actions remain behind human approval, so teams can accelerate response without giving up control.
A faster, clearer incident response workflow
By combining real-time detections in Wiz Defend with the SecOps AI Agent’s automated investigations, teams get a consistent, explainable starting point for every incident. It reduces the manual effort required to understand what happened and lets analysts focus on verification and action—not gathering context across cloud services.
Detect active cloud threats
Learn how Wiz Defend detects active threats using runtime signals and cloud context—so you can respond faster and with precision.
