SOAR playbooks: how they work, why they break, and what's replacing them

A SOAR playbook is a codified sequence of steps that executes in response to a security alert or event. The "playbook" terminology predates SOAR and comes from incident response runbooks, the written procedures analysts followed before automation existed. A SOAR platform takes that procedural logic and renders it as executable workflow, typically as a directed graph of API calls, conditional branches, enrichment steps, and human-approval gates.

The defining characteristic of a SOAR playbook is that the investigation or response logic is authored ahead of time and reused across alerts that match a trigger condition. A phishing playbook fires when a phishing-class alert arrives. An impossible travel playbook fires when the corresponding detection fires. The playbook itself is a static asset, version-controlled in the SOAR platform, executed against whatever alert arrives.

What playbooks are used for and why they exist

SOAR playbooks exist because SOC analysts spend most of their time on repetitive enrichment and response tasks that look the same across thousands of alerts. Querying an EDR for process lineage, looking up a sender domain in a threat intelligence feed, checking whether a user has MFA enrolled, opening a ticket, quarantining a host. The work is mechanical, well-defined, and high-volume, which is the profile that automation handles well.

The common categories are alert enrichment (pulling context from adjacent tools before an analyst sees the alert), triage automation (classifying or auto-closing alerts that meet specific criteria), response actions (containment steps like host isolation, account disable, email quarantine), and case management handoffs (ticket creation, notification, escalation). Some programs also use SOAR for non-alert workflows like phishing inbox processing or vulnerability ticket routing.

The original business case for SOAR was straightforward. Analyst time is expensive, alert volume scales faster than headcount, and a significant portion of analyst work is mechanical. If a playbook can do in seconds what an analyst does in fifteen minutes, the math works on paper.

{{ebook-cta}}

How playbooks are built

Playbook construction follows a roughly consistent pattern across vendors. An engineer identifies a repeatable workflow, maps the steps an analyst takes today, and translates each step into a node in the SOAR platform's graph editor. Each node is either an integration call (query Splunk, call CrowdStrike, look up an IP in VirusTotal), a transformation step (parse, normalize, format), or a control flow element (conditional branch, loop, wait-for-approval).

The integrations themselves are the heaviest dependency. SOAR platforms ship with prebuilt connectors for common security tools, and most programs supplement these with custom integrations for internal systems, niche vendors, or APIs the platform doesn't cover natively. Each integration carries its own authentication, rate limiting, and response schema.

A working playbook typically goes through a build cycle of requirements gathering with the analyst who owns the workflow, prototype construction, integration testing against representative alerts, staged rollout, and tuning against production volume.

The challenge of building them

Building a playbook that handles the happy path is straightforward. Building one that handles the long tail of edge cases is where the engineering time goes.

The hard parts are mostly about variance in the inputs. Alerts of the same type carry different fields depending on the detection that generated them. Users have different attribute shapes in the identity provider depending on how they were provisioned. Hosts appear in the EDR with different tagging conventions depending on which team deployed the agent. A playbook that assumes consistent input across the alert population will work for the majority case and fail in ways that are hard to predict for the rest.

The other structural challenge is that playbook authors have to encode investigative judgment as branching logic. An analyst looking at an alert makes dozens of small decisions about which evidence to pull next, which questions matter, when to stop investigating. Translating that judgment into a decision tree requires the author to anticipate every path in advance. The result is either a shallow playbook that handles a narrow slice of cases well, or a deep playbook with sprawling branch logic that becomes difficult to reason about.

How playbooks are maintained

Maintenance is the part of the SOAR lifecycle that gets the least attention at procurement and the most attention in operation. A playbook in production is a live dependency on every system it integrates with, and those systems change on their own schedules.

Standard maintenance activities include updating integrations when vendor APIs change, adjusting field parsing when upstream schemas shift, retuning thresholds and branch conditions when detection logic changes upstream, adding new branches when new alert variants appear, and removing or consolidating playbooks that have been superseded. Some teams formalize this with a quarterly review cycle, others handle it reactively as breakage surfaces.

The ownership pattern matters. In most programs, the engineer who built a playbook is not the engineer responsible for maintaining it three years later. The institutional context about why a branch exists, which fields were trusted, which edge cases were deliberately ignored, tends not to survive in the playbook itself. New maintainers either re-derive the context from reading the graph and the integration docs, or they make changes carefully and conservatively without fully understanding the original intent.

Why playbooks break

Playbook breakage falls into a few recurring categories.

Upstream schema and API changes. A vendor updates its detection API, deprecates a field, or changes how a value is represented. The playbook continues to execute but operates on missing or malformed data. This is the most common form and the hardest to detect, because a playbook parsing a field that no longer exists usually returns a null rather than throwing an exception. Downstream logic runs against the null, and the playbook completes without errors.

Detection logic drift. The rule that fires an alert gets retuned upstream, often by a different team. The playbook's assumptions about what the alert represents stop matching reality. A playbook built when a detection had a 30% true-positive rate behaves differently when that detection gets tuned to 70%, and the branch conditions that were calibrated against the old rate stop making sense.

Environment expansion. New SaaS apps, new identity providers, new cloud accounts, new EDR coverage. Each addition widens the space of inputs the playbook will see. A playbook authored against a single IdP handles a second IdP poorly, and the failure mode depends on how strict the original input handling was.

New attack patterns. Playbooks encode the investigative logic for threats known at authoring time. Novel techniques route down the wrong branch, get auto-closed by triage logic that wasn't designed for them, or get enriched against the wrong data sources.

Personnel rotation. Discussed above. Not a technical failure mode, but a structural one that compounds all of the others.

The reason this category of failure is consistently underestimated at procurement is that the ROI calculation for a SOAR program is usually done at authoring time. Time saved per execution times execution volume, minus engineering hours to build. The ongoing engineering hours required to keep dozens or hundreds of playbooks aligned with a changing environment rarely appear in the original case. Maintenance load also tends to be bursty rather than steady, which makes it hostile to capacity planning. A vendor release week can break three playbooks at once; the surrounding weeks look like idle capacity.

The playbooks that age best are the ones with narrow scope against stable upstream contracts. Quarantine release on a long-standing email gateway, password reset against a stable IdP, ticket handoff to a stable ITSM. Playbooks that wrap durable standards like SCIM or SAML decay slower than playbooks that wrap vendor-specific endpoints.

What dynamic investigation is and how AI enables it

A dynamic investigation generates its logic at run time against the specific alert and environment, rather than executing a pre-authored sequence. The investigation is produced fresh for each alert based on the available evidence, the questions a senior analyst would ask given that evidence, and the tools available to answer them.

The mechanism is large language model reasoning grounded in the security tool stack. An LLM with access to the SIEM, EDR, identity provider, email gateway, and other relevant tools can read the alert, decide what to ask, query the tools, evaluate the response, and decide what to ask next. The investigation path is not predetermined. Two alerts that look superficially similar can take different investigative paths if the evidence warrants it.

The relevant difference for this discussion is that there is no static playbook asset. The investigation logic does not exist in a serialized form that needs to be maintained against environmental drift. A schema change in the SIEM becomes evidence the model reasons about rather than a parsing failure. A new SaaS app in the environment becomes another tool the model can query. Novel attack patterns route through the same reasoning loop as familiar ones, because the loop is investigative reasoning rather than pattern matching.

This is not magic. Dynamic investigation has its own failure modes (hallucinated tool calls, incorrect reasoning, inconsistent decisions across similar alerts) and its own operational requirements (grounding in real tool output, observability into the reasoning, controls around what actions can be taken autonomously). The point relevant to playbook maintenance is narrower: the maintenance asset disappears, and the maintenance economics disappear with it.

AI SOC agents and what they replace

AI SOC agents are the productized form of dynamic investigation. They sit in the position SOAR has occupied for the last decade, between alert generation and analyst review, but the work they do at that position is structurally different. Where a SOAR playbook executes pre-authored steps, an AI SOC agent conducts an investigation and produces a verdict and supporting evidence.

The replacement is partial in practice. Most programs running AI SOC agents continue to use SOAR or SOAR-like infrastructure for deterministic response actions where the action is well-defined and the input is stable. Quarantine a host, disable an account, push a ticket. The work that gets displaced is the investigative work in the middle, where the variance in inputs and the judgment required were the parts that made SOAR maintenance expensive in the first place.

The buyer's case for the shift is a separate discussion. The narrower point here is that the maintenance economics of static playbooks are a real cost that most programs have not measured honestly, and the architecture that removes the static asset removes that cost. Request a demo of Prophet AI to see what alert investigation looks like when there’s no playbook to maintain.