Security orchestration, automation, and response (SOAR) runs predefined, deterministic if/then playbooks that people write and maintain, so it stalls on alerts nobody scripted. An AI-driven security operations center (SOC) uses large language model (LLM) agents to reason through each alert and investigate cases no playbook anticipated. The two work together rather than replace each other.
The divide between an AI SOC and SOAR comes down to one question about when the decision is made. That decision-making model, not a feature checklist, should drive your next tooling choice.
Because each model fits a different kind of alert, the strongest setups don't choose one. Instead, they run AI agents for the judgment calls an AI SOC makes, deterministic steps for the predictable work SOAR handles, and human review over both, all working together as one system.
The key differences in AI SOC vs. SOAR at a glance
AI SOCs and SOAR differ in when each decision gets made. In an AI SOC, an AI agent reasons through each alert at runtime, including alerts no playbook anticipated. In SOAR, a person decides each path in advance, when writing the playbook.
The table below compares the two across six dimensions:
What is an AI SOC?
An AI SOC uses AI agents to triage and investigate alerts by reasoning through the evidence, while people approve high-impact actions. Instead of following static playbooks, those agents handle enrichment, triage, investigation, and threat hunting on alerts no one scripted.
What is SOAR?
SOAR turns repeatable security tasks into playbooks, so teams handle known threats consistently, at machine speed. Playbooks stay consistent because every branch is a decision a person made in advance.
Because people write every branch by hand, the number of branches to write and maintain grows with the environment. KuppingerCole’s April 2026 Emerging AI SOC Leadership Compass describes the result as an efficiency wall for rule-based, playbook-based systems. The same report recommends judging platforms by usability and integration rather than by how many predefined playbooks they ship with.
How AI SOCs and SOAR differ in practice
The differences between an AI SOC and SOAR show up in how decisions get made, where the upkeep goes, what happens to alert noise, and how a single alert moves through each system.
How decisions are made
AI agents use contextual reasoning, weighing the evidence around an alert to decide what it means and what to check next. An agent considers factors such as the user’s normal behavior, the asset involved, and related alerts, then chooses its next tool as the evidence develops until it reaches a verdict.
SOAR works from decisions a person made in advance. A playbook follows its branches exactly, which keeps known workflows predictable but means one unexpected input can stall the whole run.
Each approach fits a different kind of work. Deterministic steps stay reliable on known tasks for the same reason they struggle on novel ones, because they never leave the script. Fixed rules are cheap, reliable, and right for repeatable tasks, while contextual reasoning fits alerts too ambiguous or novel to script.
Where the upkeep goes
SOAR upkeep centers on the playbooks themselves. Each new alert type, tool, or API version needs another branch, and connectors encode assumptions about interfaces, so teams accumulate investigative debt as those interfaces change.
With agents, teams move from scripting paths to supervising judgment. They define instructions and boundaries, check the evidence an agent cites, and correct the agent when its output drifts, which is how the Tier-1 analyst role shifts as agents take on first-pass triage.
The upkeep doesn’t disappear with agents. Instead, it moves from writing branches to reviewing decisions, which scales with alert volume instead of with every new tool and API change.
What happens to alert noise
An AI SOC uses the evidence around an alert, such as past verdicts on similar alerts, the user’s role, and recent activity on the host, to close benign alerts static rules can’t judge.
SOAR handles alerts using playbooks that match a condition and run the same fixed branch every time. They typically close only the benign patterns someone wrote a rule for, such as an allowlisted IP or VPN range, and anything outside those rules goes to an analyst or gets misrouted.
That spillover into the analyst queue is the root of alert fatigue, with analysts drowning in benign alerts novel enough to evade a static playbook. Citing its 2025 SOC Survey, SANS reports that two-thirds of SOC teams can't keep pace with alert volume, and that, in many organizations, nearly half of alerts go uninvestigated. In the SANS 2025 Detection and Response Survey, 73% of respondents named false positives as their top detection challenge.
How alerts move through each system
An AI agent can pursue evidence a SOAR playbook's fixed branches never anticipated. An impossible-travel alert, in which one user signs in from two distant locations minutes apart, shows the difference. A typical SOAR playbook for this alert runs four fixed steps:
A security information and event management (SIEM) webhook triggers the playbook.
The playbook pulls the user and manager from the directory and checks both IPs against threat intelligence reputation scores.
It calculates distance over time against a fixed speed threshold and checks an allowlist of approved IP and VPN ranges.
It messages the user in Slack to confirm the sign-in. If the user does not respond or confirm, it escalates to an analyst or disables the account in Okta, revokes sessions, and opens a ServiceNow ticket.
Every step is set in stone. Playbooks can check known VPN ranges, but if the second login came through a corporate egress point nobody added to the list, the playbook treats the login as travel. Impossible-travel alerts can generate false positives when users connect through VPNs or mobile networks.
An AI agent starts from the same trigger and data but can pursue evidence the branches don’t anticipate, such as MFA results, device compliance, and recent OAuth grants, then write a verdict with its reasoning. If the agent recommends containment, a human-in-the-loop approval pauses the workflow for a person to confirm. Deterministic steps then disable the account and revoke sessions in Okta or Entra ID exactly as written.
Does AI turn a SOAR into an AI SOC?
A SOAR with generative AI added is still a SOAR, not an AI SOC. Bolted-on features such as playbook drafting, query generation, and alert summaries save time, but the playbook still decides the path. KuppingerCole describes the category shift as a move from rule-based playbook automation to an LLM-based reasoning layer combined with specialized agents.
When you evaluate any product, ask whether the agent can choose its next investigative step or only fill a slot in a fixed sequence. Also ask whether the agent's reasoning is recorded as evidence you can check.
How AI SOCs and SOAR work together
SOC teams get the most from the full spectrum of execution, with deterministic workflows, AI agents, and human review running in one environment. Stacking an AI SOC on top of a SOAR can add a second console, a second permission model, and a handoff between the two systems. A single surface lets AI reason where judgment is needed while deterministic steps execute remediation predictably. Building that single surface comes down to how the handoffs between agents, deterministic steps, and analysts are wired.
Tines customers wire these handoffs in Tines 3B, which runs AI agents, apps, and automation in one governed environment. Each step runs in its own isolated sandbox, and a credential proxy injects secrets into outbound requests at run time, so workflow code never sees an Okta or ServiceNow credential. Teams test changes on an isolated branch before pushing them live, and every commit is kept for rollback.
Keeping humans in control with autonomy limits, guardrails, and auditability
Your team decides how much autonomy agents get, and the governance floor should be explicit. Gartner's 2026 cybersecurity trends call for human-in-the-loop frameworks in AI-supported processes. Joint guidance from CISA, NSA, NCSC-UK, and partner agencies recommends preventing agents from executing high-impact actions without prior human approval, and says designers or operators, not the agent, should decide when approval is required.
The risks are specific to security work. Attackers control many log fields an agent reads, including user agents, URLs, and attempted usernames. The "Poisoning the Watchtower" research shows how adversarial log content can steer an LLM to label a malicious event benign. Models can also hallucinate. Immutable audit logs, approval gates on destructive actions, and scoped permissions limit the damage from log injection or hallucination.
Those risks are why orchestration beats black-box autonomy. A person approves each high-impact action, and every step leaves an audit trail.
What this means for the SOAR you already run
Your existing playbooks keep doing what they do best, running well-understood response steps the same way every time. KuppingerCole's Emerging AI SOC Leadership Compass recommends that teams "Keep deterministic playbooks for well-understood incident handling rules and apply AI where rules are too costly or brittle." Phishing response often includes predictable steps that can stay deterministic.
Agents change an existing SOAR program at its decision points. Wherever a playbook escalates because no branch fits, an agent can investigate first and hand a reasoned verdict to the next deterministic step or to an analyst. Designing the handoffs between agents, deterministic steps, and analysts is central to building an AI-driven SOC.
Where Tines fits
Security teams shouldn't have to choose between the reliability of deterministic SOAR and the adaptability of an AI SOC. Teams that build on Tines 3B, the complete environment for enterprise AI, get one governed environment to run AI agents for ambiguous judgments, deterministic steps for predictable remediation, and human-in-the-loop approvals for high-impact actions.
Audit logs record every user and system action. Role-based access control is scoped per connector and network, and per space (where each team's workflows run with the connectors and permissions IT grants them). Networks and Tunnel let workflows reach systems on private networks, and bring your own model (BYOM) lets teams use their own model provider.
A practical next step is to take one high-volume alert type, such as impossible travel, and rebuild its workflow with agent triage feeding a human-in-the-loop analyst approval step. Teams can try the rebuilt workflow in Tines 3B Explore Edition, the forever-free tier with AI, SSO, and unlimited integrations.
Frequently asked questions
Does SOAR use AI?
Increasingly, yes. Gartner expects 50% of threat detection, investigation, and response platforms, including SOAR, to incorporate agentic AI capabilities by 2028, up from less than 10% in 2024, CSO Online reports. Where a SOAR product adds generative AI assistants for drafting playbooks, writing queries, or summarizing alerts, the playbook still tends to determine the path an alert takes.
Will AI replace the SOC?
Gartner frames AI SOC agents as tools that augment human operators, and KuppingerCole's April 2026 Emerging AI SOC Leadership Compass calls a fully autonomous SOC unrealistic and undesirable. AI takes on repetitive triage and enrichment, while people keep decisions about high-impact actions, ambiguous incidents, and accountability.
What's the difference between a SOC and SOAR?
A SOC is the team and function that monitors, investigates, and responds to security incidents. SOAR is technology that SOC uses to orchestrate tools and automate response through predefined playbooks.
Is an AI SOC a replacement for SOAR?
An AI SOC complements SOAR more than it replaces it outright. AI agents handle the reasoning that static playbooks cannot, but deterministic execution remains the right tool for predictable remediation steps. Most teams get better results by running both in one environment, with human approval on high-impact actions.