autobotAI

What is AutobotAI?

AutobotAI is an intelligent automation platform designed for cybersecurity and IT operations teams. It connects alerts, cloud resources, identity management elements, compliance requirements, application security features, and operational tools together, enabling teams to create workflows for investigating issues, making decisions, approving actions, and carrying out necessary corrections.

The purpose of this product is not to replace SIEMs or traditional SOAR solutions, but rather to add multi-agent orchestration, generative AI-based reasoning, and unified governance to existing security technologies. Teams can create deterministic workflows, while also enabling the automatic execution of tasks with low risk levels.

Core functions

• Multi-agent orchestration: Enables security, identity, compliance, application, and cloud operations agents to work together to handle events.
• Alarm investigation and response: Collecting context, enriching threat intelligence, and taking actions such as isolation or blocking.
• Explainable decision-making: Records the reasons behind the AI’s judgments, the steps taken during processing, and the resulting outcomes.
• Manual approval: Deletions, repairs, or permission changes with a high impact can first be submitted for human confirmation.
• Workflow construction: It offers options for no-code, low-code, and full-code development.
• Unified integration: Connects public clouds, Kubernetes, SIEM, EDR, as well as chat and ticketing systems.
• Automation of compliance: Continuous monitoring of policies, collection of evidence, and promotion of risk remediation.
• Operation governance: Provides centralized viewing of agent status, execution history, audit trails, and exceptions.

Multi-agent secure operation

The platform breaks down the capabilities in various security areas into collaborative agents; the central orchestration layer is responsible for transmitting context and enforcing governance rules. A suspicious credential event can trigger session cancellation, analysis of the impact on resources, event logging, and compliance procedures all at once.

What’s different from traditional SOAR?

Workflow and Bot Development

A bot is an automated workflow unit within a platform; it can be triggered by events, run on a scheduled basis, or called by other processes. Security analysts can use visual nodes to combine various steps, while engineers can employ Python, APIs, or the command line to add custom functionalities.

• Event trigger: Runs immediately upon receiving an alert, webhook, or system event.
• Scheduled triggering: Perform audits, cleanup, and evidence collection on a daily, weekly, or monthly basis.
• Manual trigger: Initiated by analysts as needed during investigations or emergency responses.
• Conditional branching: Select different paths based on risk, asset importance, or investigation results.
• Approval step: Wait for authorization before modifying, deleting, blocking, or changing permissions.
• Code actions: Use Python or the command line to handle non-standard systems and complex logic.
• Reusable tools: Register mature processes as capabilities that can be invoked by other agents.

How to create the first automated workflow

1. Choose security tasks with a high repetition frequency, clear criteria for judgment, and a controllable scope of impact.
2. Connects the sources of alerts, asset management systems, threat intelligence, with notification or ticketing tools.
3. Set triggers, data enrichment, conditional checks, and action responses within the workflow.
4. Manual approval steps have been added for deletion, isolation, blocking, and permission changes.
5. Use historical events or test environments to verify normal, abnormal, and failed branches.
6. Launch it first in read-only or advisory mode, and compare the agent’s judgments with those of the analysts.
7. Once the accuracy and security thresholds are met, low-risk automated processing will be gradually enabled.

Manual approval and explainability

The approval step allows the responsible person to review the relevant context before approving or rejecting requests for repairs, deletions, or changes to resources. The platform emphasizes keeping records of the reasons, steps, and outcomes of each decision, which facilitates secure post-review analysis, explanations to management, and audit purposes.

• Automated execution, post-event notification, and pre-approval are distinguished based on action risk.
• The approval information should include assets, alerts, evidence, recommended actions, and potential impacts.
• After a rejection, an alternative process should be initiated, rather than allowing the event to end silently.
• All human decisions and agent actions should be recorded in audit logs that cannot be altered arbitrarily.
• Processes with a high impact level require the configuration of emergency stop, rollback, and manual takeover mechanisms.

Integration capability

The official website states that the platform can connect to more than 600 different services, including those provided by AWS, Azure, Google Cloud, Kubernetes, code repositories, monitoring tools, messaging applications, and security products. The available actions, authentication methods, and package options must be verified for each specific connector.

Linux Agent

The Linux Agent is used to execute command-line tools and scripts in the target environment; it can run cloud CLI, Terraform, Ansible, Kubectl, and Bash commands. The official documentation specifies that Ubuntu 22.04 or a newer version is required, it supports x86_64 and ARM64 architectures, and a secure network connection to the platform is necessary.

• Proxy services require high system permissions; it is necessary to assess host isolation and minimum permissions before installation.
• Credentials and configurations should be managed by a controlled key system; they cannot be included in ordinary scripts or tickets.
• Debug logs may contain tokens or internal state; detailed logging levels should not be used for an extended period in production environments.
• Verify in the testing environment before making any updates, to prevent automatic upgrades from affecting critical response processes.
• Create an allowlist for executable commands and limit the range of resources that the proxy can access.

Security and compliance scenarios

• Alarm noise reduction: Prioritizes low-severity alarms, disables clear false alarms, and elevates high-risk incidents.
• Fishing response: Analyze the email, extract metrics, search for similar emails, and request approval for isolation.
• Cloud configuration governance: Identifies public storage, overly permissive security groups, and incorrect identity policies.
• Vulnerability remediation: Identify the responsible person, create a ticket, and continuously monitor and verify the fix.
• Access governance: Identify unused accounts, excessive permissions, and temporary accesses that are about to expire.
• Compliance evidence collection: Regularly gather control evidence and document exceptions and risk acceptances.
• Key governance: Detect leaked or expired keys, coordinate their replacement, and verify that the old credentials are no longer valid.
• Certificate management: Monitor expiration dates, facilitate renewal, and verify deployment status.

Recommended deployment process

1. Identify a use case with a clear business objective, such as reducing the average processing time for level 1 alerts.
2. Sort out the data sources, existing tools, account permissions, and approval responsibilities.
3. Connect a test account in an isolated environment and import a small number of historical events to carry out concept validation.
4. Define actions that cannot be executed automatically, the limits of maximum permissions, and the procedures for handling failures.
5. Compare the manual conclusions with those generated by the agent, and record the accuracy rate, false positives, and false negatives.
6. Complete security reviews, penetration testing, log auditing, and disaster recovery validation.
7. Launch in phases while continuously monitoring costs, response times, and the quality of automated handling.

Packages and pricing

The official website currently presents its plans categorized as Community, Standard, and Enterprise; however, it does not specify the fixed prices for the Community and Standard tiers in the public price listings. The first two tiers offer a 14-day free trial, while Enterprise plans come with customized quotes, and the final cost is determined based on the purchase or contract details.

The execution volume shown on the package page represents the capacity limit; it is not equivalent to the number of alerts or workflows. When estimating the costs, it is also necessary to take into account cloud resources, third-party APIs, model calls, log storage, implementation services, and internal maintenance staff.

How to choose a package

• Small security and IT teams can first use Community to validate two cloud integrations and one high-frequency process.
• Standard is more suitable for evaluation in multi-cloud or hybrid environments where a complete set of security tools is required.
• Global organizations, managed security service providers, and environments with high compliance requirements usually need Enterprise.
• During the trial period, the actual amount of work carried out should be taken into account, to avoid choosing a solution based solely on the number of team members.
• Before purchasing, verify the excess charging, model costs, data storage, and support response levels.

Which teams are suitable?

• Security operation centers that have to deal with a large number of repetitive alerts on a daily basis, leading to analyst fatigue.
• Companies that manage AWS, Azure, Google Cloud, and on-premises infrastructure simultaneously.
• Organizations that require ongoing identity governance, access reviews, and principle of least privilege.
• Compliance and risk teams that are required to automatically collect evidence and drive corrective actions.
• We aim to create teams in which analysts use low-code tools and engineers use Python to develop processes together.
• Managed security service providers that offer multi-tenant capabilities, centralized governance, and customer-specific customization options.

Product advantages

• It covers multiple areas including security, identity, compliance, applications, cloud, and IT operations.
• It offers a progressive approach to development, ranging from no-code to full code.
• It places emphasis on approval, auditing, explainability, and human oversight, making it suitable for critical security scenarios.
• It enables the addition of orchestration capabilities on top of existing SIEM, SOAR, EDR, and cloud platforms.
• It supports connection methods such as API, Webhook, CLI, Linux Agent, and agent protocols.
• The publicly available documentation is quite comprehensive, which facilitates the technical team in assessing the architecture and operational requirements upfront.

Usage restrictions and risks

• Multi-agent systems are complex to configure, and require joint governance by security, platform, and business teams.
• The automatic processing rates and efficiency figures displayed on the official website cannot replace verification conducted by our own company.
• If high-privilege connectors or the Linux Agent are configured incorrectly, it may expand the scope of the impact of the operations.
• Generative AI can still make mistakes; critical actions related to isolation, deletion, and permissions must remain subject to approval.
• The presence of over 600 integrations does not mean that each connector covers all actions and enterprise versions.
• The public page does not specify the fixed prices for the first two tiers; the overall cost requires a quote and preliminary calculations.
• When a large volume of logs and sensitive alerts arrive, it is necessary to examine separately the data storage locations and the paths used for model processing.
• The platform cannot replace SIEM, EDR, vulnerability scanning, nor an organization’s own security responsibilities.

Security governance checklist

GitHub and the open-source status

The AutobotAI core platform is not an entirely open-source project. The official affiliated organizations have made available repositories such as the Terraform Provider, integration projects, and runtime environment extensions, which can help users understand some of the methods of integration; however, this does not mean that the code for agent orchestration, the management interface, or the server-side components is available in an open source form.

Basic information

Recommendation score

The comprehensive recommendation score is 4.4 out of 5 points. AutobotAI offers a comprehensive set of functions and a well-thought-out governance framework, making it suitable for medium to large teams that have specific needs in terms of security automation; however, the complexity of deployment, permission-related risks, and the overall cost must be evaluated through pilot projects.

Frequently Asked Questions

What is AutobotAI mainly used for?

It helps security and IT teams to build, manage, and operate intelligent workflow agents that enable investigation, decision-making, and execution of actions.

Can it replace SIEM or SOAR?

Its official role is one of collaboration and enhancement; it can be integrated with existing SIEM, SOAR, and EDR systems, but it should not replace them entirely by default.

Is programming required?

Not necessarily; analysts can use visual workflows, while engineers can leverage Python, APIs, and CLI for expansion.

Will high-risk actions be executed automatically?

Approval can be based on risk assessment; actions with a high impact should require manual confirmation and must allow for the possibility of rolling back.

Which cloud platforms are supported?

The official options mainly include AWS, Azure, and Google Cloud; Kubernetes as well as local Linux environments are also supported.

Is there a free trial?

Both Community and Standard offer a 14-day free trial; the specific requirements for activating it can be found on the purchase page.

How much is AutobotAI?

The public package page does not list the fixed prices for the first two tiers; Enterprise plans come with customized quotes, and the cost needs to be determined based on the volume of work and the scope of integration.

Is manual approval supported?

Supported: Approval or rejection nodes can be added to workflows for fixing, deleting, and making changes to resources.

Is there an official GitHub?

There are publicly available Terraform Providers and integration repositories from related organizations, but the core platform code is not made fully available to the public.

Is it suitable for small teams?

Small teams with clear, high-frequency tasks can start with the Community pilot, but when the workload is low, it is necessary to weigh the costs of platform management against the actual benefits.