SOAR vs AI: Which Approach Actually Automates Security Operations?

What SOAR Was Supposed to Do (and Why It Partly Failed)

Security Orchestration, Automation and Response (SOAR) emerged as a category around 2015–2017, promising to solve the alert volume crisis through automation. The underlying idea was sound: security workflows are repetitive, and repetitive work can be codified into playbooks that execute automatically. Enrich this IP. Query this user's activity. Send this Slack notification. Block this hash.

SOAR platforms delivered on that promise in specific, narrow use cases — particularly for response actions where the outcome is deterministic. If a hash is in your malware blocklist, block it. That logic works reliably because the decision is binary and the outcome is well-defined.

Where SOAR failed was in the analytical layer — the work of deciding whether an alert represents a real threat. Security analysis requires contextual judgment. Is this login anomalous? That depends on who the user is, where they normally log in from, what they normally do after logging in, what else is happening in the environment at the same time, and whether the asset they're accessing is sensitive. No static playbook handles that kind of multi-dimensional context well.

How Traditional SOAR Playbooks Work

A SOAR playbook is essentially a directed acyclic graph of conditional steps. When a trigger condition is met — an alert fires, an email arrives, a ticket is created — the playbook executes a sequence of actions: API calls to enrichment services, conditional branches based on response values, notification actions, and potentially response actions like account disabling or firewall rule creation.

The steps themselves are usually implemented as integrations with specific tool APIs. "Look up this IP in VirusTotal" becomes an authenticated HTTP request to the VirusTotal API, with logic to parse the response and extract the malicious flag and category. "Get the user's manager from Active Directory" becomes an LDAP query or Microsoft Graph API call. Each integration must be built, tested, and maintained.

For mature SOAR deployments with dedicated engineers, this architecture works reasonably well for the use cases the playbooks cover. The problem is what it doesn't cover — and what happens when covered scenarios change.

The Brittleness Problem: Why Playbooks Break

Playbook brittleness manifests in several predictable ways:

• API version changes: A vendor updates their API and the response schema changes. The playbook's parser breaks silently, returning null values or incorrect data without the playbook necessarily erroring out. Analysts receive enrichment fields that say "undefined" without knowing why.
• Authentication changes: API keys expire, OAuth flows change, MFA is enforced on service accounts. Integration authentication breaks and the playbook fails to execute.
• Alert format drift: The SIEM updates and the field names in alert payloads change. Playbook triggers that look for specific field names stop firing.
• Logic gaps: Playbooks only handle scenarios that were anticipated when they were written. A new attack technique that doesn't match the trigger conditions simply doesn't get processed. The alert sits in the queue unautomated.
• Coverage debt: As the detection catalog grows, the automation catalog can't keep pace. Teams end up with hundreds of alert types and playbooks covering only a fraction of them.

What AI-Native Security Automation Does Differently

AI-native security automation replaces the playbook-based analytical layer with a model that can reason about context without pre-scripted logic. Instead of "if field X equals value Y, then look up Z," the AI layer is given a set of tools and data sources and asked to determine relevance, assess risk, and explain its reasoning.

This architecture handles novel scenarios without requiring a new playbook to be written. When a new attack technique emerges, the AI layer can analyze alerts generated by new detection rules using the same contextual reasoning it applies to familiar patterns. The coverage gap between detection and automation shrinks dramatically.

The AI also handles the multi-dimensional context problem that defeats static playbooks. It can simultaneously consider user behavior history, asset criticality, threat intelligence context, related events in the same timeframe, and environmental factors — and produce a coherent risk assessment that reflects all of those dimensions.

Use Case 1: Alert Triage — SOAR Approach vs AI Approach

SOAR Approach to Alert Triage

A SOAR playbook for alert triage might: extract the source IP from the alert, look it up in VirusTotal, check if the IP is in a known bad IP list, extract the user account, check if the account is privileged, and assign a severity based on the combination of those lookups. If IP is clean and user is not privileged, close as low severity. If IP is malicious, escalate.

This works for alerts that exactly match the playbook's assumptions. It fails for: alerts from sources not in the playbook's trigger conditions, alerts where the relevant indicator isn't an IP, or alerts where the combination of non-malicious signals constitutes a real threat (which is exactly how sophisticated attackers operate — they use clean infrastructure and legitimate accounts).

AI Approach to Alert Triage

An AI-native triage layer receives the full alert context — all fields, related events in the same session, user behavioral history, asset context — and produces a reasoned disposition with an explanation. It is not limited to checking specific fields. It can identify that an alert's source IP is clean but the combination of timing, target asset, and user behavior is highly anomalous. It can handle alert types it hasn't explicitly been trained on because the reasoning approach is general, not case-specific.

Use Case 2: IOC Enrichment — SOAR Approach vs AI Approach

SOAR Approach to IOC Enrichment

SOAR playbooks for IOC enrichment query a predetermined list of threat intelligence sources and append their responses to the alert. The analyst receives a field-by-field dump of API responses: VirusTotal score, Shodan open ports, AbuseIPDB reports, WHOIS registration date. The analyst must synthesize these independent responses themselves.

AI Approach to IOC Enrichment

An AI-native enrichment layer queries the same sources and synthesizes them into a coherent narrative: "This IP registered in March 2026 is hosted on a bulletproof hosting provider with no legitimate business history. It appeared in 3 threat reports in the last 30 days associated with initial access broker activity. Combined with the destination port and payload pattern, this is consistent with a reconnaissance scan from a commercially available attack framework." That's analysis, not just data.

Use Case 3: Incident Scoping — SOAR Approach vs AI Approach

SOAR Approach to Incident Scoping

SOAR incident scoping playbooks typically pull a predetermined set of related events: other alerts involving the same user, other alerts involving the same host, firewall logs for the source IP in the last 24 hours. The output is a set of raw log lines and alert records that an analyst must manually review to determine scope.

AI Approach to Incident Scoping

AI-native incident scoping identifies the blast radius dynamically. It determines which entities are relevant based on their relationship to the triggering event, not based on a static list of lookup types. It can identify that a compromised account accessed a shared service that 12 other users depend on, and that one of those users has exhibited unusual behavior in the same timeframe — and present that as a scoping finding without requiring a playbook that anticipated that exact relationship graph.

Where SOAR Still Makes Sense: Response Actions, Not Analysis

SOAR's strengths are deterministic, well-defined response actions where the decision has already been made and the execution just needs to happen reliably and with an audit trail:

• Disabling a compromised user account in Active Directory when an analyst approves the action
• Blocking an IP on a firewall or WAF when a threat is confirmed
• Creating a ticket in ServiceNow or Jira with populated fields from the alert
• Sending a Slack or PagerDuty notification with formatted alert details
• Quarantining an endpoint via EDR API when malware is confirmed
• Collecting forensic artifacts from a host (memory dump, log export) as part of a confirmed incident workflow

These use cases are SOAR's strongest domain. The steps are repeatable, the APIs are stable, and the outcomes are binary. The failure mode of a broken playbook here is visible (the action didn't happen) rather than silent (the analysis was wrong).

The Hybrid Approach: AI Analysis + SOAR Response

The most effective architecture combines AI-native analysis with SOAR-based response orchestration. The AI layer handles everything that requires judgment — triage, enrichment, scoping, prioritization. SOAR handles everything that requires reliable execution — account disabling, firewall rules, ticket creation, notifications.

This hybrid model gets the best of both: the AI layer handles the high-variability analytical work without requiring playbooks, while the SOAR layer handles response actions with the reliability and audit trail that deterministic execution provides.

The integration point between the two is the human analyst approval step. AI analysis concludes that a user account is compromised and recommends disabling it. A human approves. The SOAR layer executes the disable action and logs the evidence chain: who approved, when, what the AI analysis concluded, what actions were taken.

How ZonForge Sentinel's Automation Model Works

ZonForge Sentinel is designed around the hybrid model. The AI SOC Analyst handles triage, enrichment, and scoping without playbooks — reasoning about each alert using live context from behavioral baselines, threat intelligence, and related event history. Response actions are presented to analysts as recommendations with supporting evidence, with execution handled through configurable response integrations.

The result is security orchestration that adapts to novel threats rather than breaking when they don't match a known pattern. SOC teams using Sentinel shift from maintaining playbooks to validating AI recommendations and approving high-confidence response actions — a fundamentally different and less fragile operational model.

SOAR vs AI: Side-by-Side Comparison

Capability	SOAR Approach	AI-Native Approach
Alert Triage	Playbook with predetermined field lookups; misses novel patterns	Contextual reasoning across all fields; handles new alert types without new playbooks
IOC Enrichment	Appends raw API responses from configured sources; analyst synthesizes	Synthesizes multi-source data into coherent narrative with confidence assessment
Incident Scoping	Pulls predetermined related-entity queries; analyst identifies scope from raw data	Dynamically identifies relevant entities based on relationship to triggering event
Case Management	Creates and populates tickets via API; reliable and auditable	AI-generated case summaries with auto-populated context; integrates with existing ticketing
Response Actions	Strong — deterministic execution, audit trail, reliable for defined scenarios	Best paired with SOAR execution layer; AI recommends, SOAR executes, human approves

Conclusion: It's Not SOAR vs AI — It's Analysis vs Execution

The framing of SOAR vs AI automation is a false binary. The real distinction is between analytical work (which AI does better) and execution work (which SOAR does better). Organizations that have tried to use SOAR for analysis — building complex decision trees to simulate judgment — have found it brittle, expensive to maintain, and ultimately inadequate for novel threats.

The right model uses AI-native analysis to handle the judgment-intensive work without playbooks, and uses SOAR-style execution for response actions where reliability and audit trails matter. ZonForge Sentinel is built on that model — giving SOC teams automation that doesn't break when attackers change their behavior.