AI SOAR for Autonomous Response and Orchestration
Spharaka AI SOAR automates the full incident response lifecycle with dynamic, AI-generated playbooks, autonomous remediation, and integrated case management, all inside the unified AI SOC platform.
Autonomous Incident Response
Eliminate manual ticket toil. Spharaka AI SOAR investigates and resolves security events end to end, with human oversight for high-impact actions only.
- AI-generated, dynamic playbooks tailored to each security event
- Automated response across endpoints, identity, network, and cloud
- Incident automation with built-in evidence and audit trail
- Case management with collaboration, SLAs, and reporting
- Workflow automation with low-code editor for custom logic
- Security orchestration across 200+ enterprise integrations
Closed-Loop AI SOC Automation
AI SOAR is natively unified with AI SIEM and AI UEBA so detection, decision, and action happen in one autonomous loop.
- Closed-loop autonomous detection and response
- Human-in-the-loop approvals for high-impact actions
- Continuous learning from analyst feedback
- Pre-built playbooks for ransomware, phishing, insider threats, and lateral movement
How a conclusion becomes an action, and what stops it
The dangerous half of autonomy is this one. Every step below exists because an automated action that is wrong causes an incident of its own.
Receive a conclusion, not an alert
Response begins from an investigated case with an evidence chain, not from a rule firing. This matters because the quality of an automated action is bounded by the quality of the judgement behind it, and acting on an unvalidated alert is how automation earns its reputation.
Generate the playbook for this incident
The response is assembled from the case in front of it: which hosts, which identities, which cloud roles, in which order, and what has to be true before each step. A pre-written playbook encodes the incident its author imagined, which is why the library ages and why novel cases fall through to manual work.
Check it against policy
AirWatch resolves each proposed step into one of three categories: permitted autonomously, requires approval, or prohibited. Prohibited stays prohibited regardless of model confidence. The check happens per step, so a case can execute four actions and stage a fifth.
Execute what is permitted
Permitted steps run immediately across endpoint, identity, network and cloud through the integrations already in place. Each action records what it did, to what, and on the basis of which evidence, before the next step begins.
Stage what is not
An action needing approval is presented with the evidence and the recommendation to a named approver, in the case rather than in a separate tool. The approver is deciding on a conclusion someone else already reached, which is a far faster decision than starting an investigation.
Verify and roll back
After execution the platform confirms the action took effect and watches for the consequence. Reversible steps carry their reversal, so a containment that turns out to be wrong is undone from the same case that ordered it.
What it can act on, and what governs it
A response platform is worth exactly what it can reach. Everything else is a notification with extra steps.
Actions available
- Host isolation, process termination and file quarantine
- Session revocation, credential disablement and token invalidation
- Firewall, DNS and proxy blocking
- Cloud role, key, snapshot and workload actions
- Mailbox actions, including message recall and sender blocking
- Ticket creation, enrichment and closure in ITSM
Where it connects
- Endpoint agents, including Spharaka EdgeProtect and third-party EDR
- Identity providers and privileged access management
- Firewalls, proxies and network access control
- Cloud provider APIs across the major platforms
- Ticketing, ITSM and on-call paging
- An existing SOAR, where one is staying in place
What bounds it
- Per-action AirWatch policy across three categories
- Asset class rules, so production and clinical systems differ
- Time-of-day and change-freeze windows
- Named approvers per action class
- A complete record of actions taken and actions declined
Around two hundred enterprise integrations ship with the platform, and a low-code editor covers the cases they do not. The integration list is the practical ceiling on autonomous response, so it is worth checking against your own stack early.
AI SOAR against conventional SOAR
The distinction is where the decision is made. Everything else in the table follows from that one difference.
| Aspect | Conventional SOAR | Spharaka |
|---|---|---|
| Where the decision happens | In a decision tree a person wrote in advance, so coverage stops at what that person anticipated. | In the investigation, from the case in front of it. The playbook is an output rather than an input. |
| What triggers it | An alert. If the alert was wrong, the automation is confidently wrong at machine speed. | An investigated conclusion with an evidence chain, which is a much better thing to act on. |
| Maintenance | A playbook library that ages as the estate changes, and quietly stops matching reality between reviews. | Nothing to age. The environment is read at the time of the incident rather than encoded months earlier. |
| Novel incidents | No matching playbook, so it falls through to the manual queue. The cases most worth automating are the ones automation misses. | Handled the same way as familiar ones, because the response is constructed rather than retrieved. |
| Safety model | Safety is a property of how carefully each playbook was written and reviewed, one at a time. | Safety is a policy that applies to every action regardless of which case proposed it. |
| Change control | Every playbook change is a change to review, so the library either goes stale or consumes the team. | The policy envelope is the unit of review, so governance happens once and applies everywhere. |
The same containment, with and without a matching playbook
Confirmed credential compromise on a privileged account, out of hours, with lateral movement already underway.
The playbook library holds a credential-compromise runbook written eighteen months ago. It disables the account and raises a ticket. It does not know about the cloud role the account assumed last year, because that role did not exist when the runbook was written.
The account is disabled. The attacker's existing cloud session, established through the assumed role, is unaffected and continues for another forty minutes until the morning shift reads the ticket.
The case is read as it stands: this account, these active sessions, this assumed role, these two hosts it authenticated to in the last hour. The response is built around what is actually true now.
Session revocation, credential disablement and cloud role suspension run in sequence, each verified before the next. Isolating the two hosts is inside policy for this asset class and executes as well.
Disabling the service account the attacker also touched sits outside the envelope, because it underpins a payment batch. It is staged with the evidence and paged to the on-call lead.
Each action is confirmed to have taken effect. The cloud session is gone rather than merely instructed to go, which is a distinction that only shows up when it fails.
The difference is not speed. Both platforms act in seconds. The difference is that one acted on the estate as it was eighteen months ago and the other on the estate as it is, and the gap between those two is where the attacker kept working.
What to test before you let anything act
Automation that is wrong is worse than no automation. These four tests are about the failure modes rather than the demonstrations.
Show me an action it declined
Ask to see cases where the platform reached a conclusion and did not act, and why. A platform with no examples either has no policy boundary or is not really deciding. Both are worth knowing before production.
What happens when an action fails?
Break an integration mid-trial and watch. Does the platform notice the action did not take effect, does the case reflect it, and does anyone get told? Silent failure is the most common gap and the hardest to find later.
Can it undo what it did?
Test a reversal on a reversible action. Containment will occasionally be wrong, and whether the reversal is one step from the same case or a manual scramble across four consoles decides how much autonomy you can safely enable.
Who signs off the envelope, and how often?
The policy is the real control, so ask how it is authored, reviewed and versioned, and what an auditor is shown. If the answer is a settings page with no change history, the governance story is a slide rather than a mechanism.
Frequently asked questions
What is AI SOAR?
AI SOAR is an AI-powered Security Orchestration, Automation, and Response platform that uses agentic AI to investigate security events, decide on remediation, and execute playbooks autonomously.
How does Spharaka AI SOAR differ from legacy SOAR?
Legacy SOAR depends on hand-built playbooks and rules. Spharaka AI SOAR dynamically generates and adapts playbooks for each event using a cybersecurity LLM, enabling true autonomous response.
Is human oversight still possible?
Yes. Spharaka AI SOAR supports human-in-the-loop approvals for high-impact actions, with full audit trails for every autonomous decision.
Explore related capabilities
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Related use cases and guides
One conclusion branching into four actions at a policy gate: two cross, one is held for approval, one is stopped.
Autonomous containment before encryption spreads.
Insider Threat
Response workflows for identity-driven risk.
Evaluating Autonomous Defence
The tests that separate autonomy from automation.
See Spharaka AI SOAR in action
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