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Cloud security

Tami ✓

Turn cloud findings into safe remediation

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AI-based Security Cloud Security Remediation Tools
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Multi Cloud Support Hybrid Cloud Support Cloud Security Enhancement Cloud security
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Verdict

A quick read before you dig into the full page.

Best if
Turn cloud findings into safe remediation
Skip if
No clear onsite continuous learning
Pick this when
Cloud security enhancement

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Overview

Tami is an AI-based cloud security agent at the core of Tamnoon's security solutions. Its main functionality is to aggregate data, conduct deep investigations, and generate remediation to aid cloud security engineers in focusing on safe remediation.

Categorized as a context-aware AI, Tami provides support for companies using a CNAPP or CDR by handling everyday security tasks. It consists of four different agents working in tandem to orchestrate and execute remediation plans.

These include the 'Context Agent', 'External MCP Agent', 'Code Generation Agent', and 'Automation Retriever Agent'. Tami uses multiple Language Models (LLMs) selected for specific tasks and utilises a human CloudPros verification process for the logic and safety of proposed solutions.

Tami also tackles the challenge of response formulation in the cloud security industry by automatically creating validated fixes for any risk it encounters.

It's been trained on Tamnoon's playbooks, which allows it to learn from best practices and create innovative approaches to unique scenarios. It works proficiently, whether the context is single-cloud, multi-cloud, or hybrid, generating comprehensive, provider-specific remediation steps.

Finally, Tami collaborates closely with expert supervision to deliver agent-led remediation scaled with AI to help teams achieve safe remediation and enhance cloud security.

Tami website

Access & connections

Where you can use Tami, and what it plugs into.

Platforms

Web

Integrations

MCP

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • Tabular

Output

  • Text

Pros & cons

Pros

  • Cloud security enhancement
  • Data aggregation capability
  • In-depth risk investigation
  • Context-aware technology
  • Supports CNAPP and CDR
  • Orchestrates remediation plans
  • Agent diversification
  • Automated response formulation
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  • Human verification process
  • Training on Tamnoon's playbooks
  • Versatile multi-cloud support
  • Flexible hybrid cloud support
  • Provider-specific remediation steps
  • Facilitates agent-led remediation
  • Creates innovative solutions
  • Safe remediation focus
  • Proficient in different contexts
  • Collaboration with expert supervision
  • Learn from best practices
  • Automatically creates validated fixes
  • 24/7 Cloud SOC support
  • Tool-agnostic security
  • Full-cycle remediation
  • Agentic cloud security capability
  • Backed by human experts
  • Detailed Reporting and Compliance
  • Agentic Investigation

Cons

  • Dependent on multiple agents
  • Needs human verification process
  • Trained only on Tamnoon's playbooks
  • No specified support for non-CNAPP/non-CDR
  • Consists mainly of non-generic LLMs
  • Heavy reliance on expert supervision
  • No clear onsite continuous learning
  • Fixed, limited models utilization
Show 2 more
  • Not identifiable multi-cloud support details
  • Non-disclosed solution for single-cloud context

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Releases 1

Initial release June 12, 2025 Current
Initial release of Tami.

FAQ 12

What is the main function of Tami?
Tami's main function is to aggregate data, conduct deep investigations, and generate remediation to support cloud security engineers focusing on safe remediation.
How does Tami support companies using CNAPP or CDR?
Tami supports companies using a CNAPP or CDR by handling day-to-day security tasks. It assists in aggregating data, conducting deep investigations, and generating remediation, thus enabling the companies to maintain a safe network environment.
Can you explain the role of each agent in Tami?
Tami is composed of four different agents with specific roles. The 'Context Agent' is the main orchestrator of initiatives. 'External MCP Agent' communicates with external CNAPPs and CDRs using the MCP protocol. The 'Code Generation Agent' learns from Tamnoon's experts and playbooks, then expands on its own. Lastly, the 'Automation Retriever Agent' selects or creates the automation to run and then analyzes the result.
How does Tami use multiple Language Models?
Tami uses multiple Language Models (LLMs) that are selected for specific tasks. These models may be trained on Tamnoon's two million-plus remediations or can be generic LLMs with deep guardrails. While the main action is carried out by the trained model, secondary models quality assure the output to meet Tamnoon's standards.
What is the CloudPros verification process in Tami?
The CloudPros verification process in Tami is a human verification procedure designed to ensure the logic and safety of proposed solutions. This system enables Tami to resolve numerous alerts without causing any outages.
How does Tami tackle response formulation in cloud security?
Tami tackles response formulation in cloud security by automatically creating detailed, contextual, and validated fixes for any risk it encounters. This innovative approach solves the industry's challenge of having powerful detection tools but struggling with formulating responses.
How has Tami been trained by Tamnoon?
Tami has been trained on Tamnoon's playbooks, written by CloudPros who specialize in major cloud providers, CNAPPs, and CDRs. These playbooks allow Tami to learn from best practices, thus enabling it to innovate and tackle unique scenarios where no exact solution is available.
How does Tami operate in different cloud contexts?
Tami operates proficiently in different cloud contexts--whether single-cloud, multi-cloud, or hybrid-cloud. It generates thorough, provider-specific remediation steps for each unique context.
What is agent-led remediation in Tami?
Agent-led remediation in Tami refers to the process of navigating and solving security challenges under the guidance and oversight of Tami's four integrated agents. This process is supplemented with artificial intelligence to achieve safe remediation and effectively enhance cloud security.
What specific tasks are Tami's Language Models selected for?
The specific tasks that Tami's Language Models are selected for involve mainly the aggregation of data, deep investigation, and remediation generation in the context of cloud security.
How does Tami help enhance cloud security?
Tami enhances cloud security by creating validated fixes for any risk it encounters. It reduces the vulnerability of the cloud by conducting deep investigations and automatically responding with expert-directed remediations.
How does Tami automatically create validated fixes for the risks it encounters?
Tami automatically creates validated fixes for the risks it encounters by applying its trained language models to analyze the risk, formulate an appropriate response, and validate its effectiveness before implementation.
Tami
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