Security teams use Kindo and Deep Hat to drastically accelerate incident response, reducing the time to investigate advanced persistent threats (APTs) from days to under an hour. The platform is also utilized to run automated red and blue team exercises, perform continuous risk assessments natively via triggers or schedules, and autonomously aggregate data from multiple sources to generate comprehensive security compliance documentation.

To prevent context overload, Kindo uses a two-step filtration process where an underlying LLM first filters the massive list of available tools down to a highly relevant subset for the Deep Hat model. If the agent makes a mistake or receives an error from a tool, the agentic loop self-corrects. It feeds the error message back into the model as new context, allowing the AI to autonomously adapt, adjust its parameters, and successfully retry the action.

Deep Hat v2 uses a Mixture of Experts (MoE) architecture, activating only a specialized subset of weights for any given prompt. This drops end-to-end inference speeds by as much as half while remaining highly memory efficient. As a result, Deep Hat v2 supports an 8x larger context window—jumping to 256,000 tokens on the same hardware. This unlocks fully agentic tool calling, enabling the AI to seamlessly ingest and process massive amounts of unformatted data without 'context exceeded' errors.

Kindo is built on a security-first foundation with comprehensive administrative controls. Administrators can explicitly dictate which tools are allowed and block access to dangerous rights or sensitive systems. Additionally, Kindo enforces robust 'human-in-the-loop' workflows; the AI can formulate a plan and queue up a tool call, but a human administrator must review and approve the action before it is executed, preventing catastrophic automated errors.

Kindo prioritizes enterprise privacy by offering a fully self-managed deployment model. Because Deep Hat is an in-house proprietary model, customers can deploy the entire Kindo stack—including the AI model itself—within their own secure environment. This ensures that highly sensitive organizational data, logs, and security telemetry never need to be sent out to third-party AI providers.

Deep Hat is Kindo's proprietary, uncensored red team cybersecurity AI model. While general foundation models lack specialized DevSecOps knowledge and are restricted by heavy censorship, Deep Hat is specifically trained for the cybersecurity domain. Its uncensored nature allows security teams to actively simulate advanced persistent threats (APTs), perform penetration testing, and discover vulnerabilities without triggering the restrictive refusal mechanisms common in commercial AI models.

Unlike traditional SOAR platforms that rely on brittle, manually built static workflows, Kindo utilizes dynamic runtime AI. Users simply describe their high-level objectives in natural language, and the AI autonomously navigates the required tools to gather data and resolve alerts. This dynamic adaptability allows organizations to replicate, exceed, and even replace legacy SIEM and SOAR capabilities while significantly reducing overall security spend.

Yes. Beyond SecOps, Aireon empowered non-technical staff to build custom AI agents. For example, their marketing team deployed an agent to generate marketing newsletters, and HR/Training teams deployed adaptive learning modules tailored to individual employee learning styles.

Because Aireon operates critical infrastructure, they utilize Kindo in a self-hosted deployment configuration to maintain strict control over their proprietary, sensitive corporate data. They also maintain strict 'human-in-the-loop' controls to verify AI-generated insights and actions before execution.

Aireon saved roughly $700,000 in a single year by cutting their security spend on redundant or legacy tools they no longer needed. Additionally, their marketing team avoided $40,000 to $50,000 in commercial software costs by using AI agents to build out newsletters and brand awareness tools internally.

No. According to CISO Pete Clay, AI did not take jobs away; instead, it acted as a massive force multiplier. A team of five people is now able to do the work of 25 to 30 people, making the existing staff significantly more effective and proactive.

By utilizing Kindo's DPAT v2 and specialized AI agents, Aireon reduced their threat hunt spin-up time from 3-4 days to just 45 minutes. Furthermore, instead of conducting manual, bespoke penetration tests on a quarterly or semi-annual basis, they now run automated penetration tests every 24 hours.

Aireon's SecOps team struggled with slow, highly manual processes. It took 3-4 days to organize a team for threat hunting against new Advanced Persistent Threats (APTs), and writing required cybersecurity compliance documentation took 6-8 months, tying up skilled staff and creating dangerous operational bottlenecks.

Aireon is a global commercial airline data tracking company. They capture highly sensitive flight data globally, transmit it via a satellite constellation to a ground station, and deliver it to air traffic control in just 4 to 6 seconds. Their environment processes roughly 140 million events a day, requiring extremely high data integrity and availability.

Instead of relying on point-in-time, bi-annual penetration testing, AI allows for continuous, dynamic security posture management. AI-driven agents can run automated red team tests daily, validating defenses against new vulnerabilities in near real-time.

A Minimum Viable Developer is a concept stemming from internal enablement programs that give non-technical staff or junior analysts the ability to use natural language AI interfaces to build custom automated workflows and scripts, bypassing traditional engineering bottlenecks.

Agentic AI can rapidly ingest vast amounts of organizational telemetry, assess vulnerabilities, and map them to compliance frameworks autonomously. This reduces the time required to produce a comprehensive risk document from several months down to hours.

Automating stupid refers to the danger of connecting automated response engines to messy, uncontextualized data (dirty telemetry). If a legacy SOAR acts on a false positive without reasoning, it can mistakenly block critical infrastructure. AI mitigates this by applying contextual reasoning before taking action.

No. AI is designed to eliminate mind-numbing busywork like compiling compliance binders and writing static playbooks. It empowers SOC analysts by turning them into 'Minimum Viable Developers' who can leverage AI to write custom workflows, elevating them to higher-value strategic tasks.

Agentic AI natively handles data ingestion, contextual reasoning, and orchestrated response, which traditionally required multiple overlapping legacy tools. By centralizing these capabilities, organizations can deprecate legacy SIEM and SOAR licenses, drastically cutting tool spend.

Legacy SOAR relies on 'Build-Time AI' and static visual playbooks that execute deterministically and break when variables change. Agentic AI uses 'Run-Time AI' to dynamically select tools, adapt to unexpected data schemas, and autonomously self-correct errors on the fly.

Self-hosting ensures that sensitive network topologies, proprietary code, and vulnerability telemetry are not transmitted to third-party, public API providers. Running models within an internal Virtual Private Cloud (VPC) protects data privacy and enables strict Data Loss Prevention (DLP) and audit logging.

Uncensored models drastically reduce operational timelines by automating complex correlations and script writing. For example, by utilizing an uncensored model, Aireon reduced the time needed to spin up a hunt routine against a new Advanced Persistent Threat (APT) from 3-4 days to just 45 minutes.

It is a security framework where an AI agent can autonomously gather context, write scripts, and stage API calls, but is hard-coded to halt before execution. A human administrator must review the AI's intended action and explicitly grant approval before the tool or script is deployed against the infrastructure.

Yes, provided the deployment utilizes a robust, security-first architecture. Giving an AI access to a "Terminal as a Cyber Control Plane" is safe when governed by "Human-in-the-Loop Agentic Execution," which ensures the AI cannot execute any write or destructive actions without explicit human approval.

An uncensored AI model has had commercial safety filters removed, allowing it to freely analyze, generate, and orchestrate offensive security code. This enables enterprise security teams to utilize the model for autonomous red-teaming, nation-state adversary simulation, and proactive zero-day fault prediction.

Commercial AI models use strict safety guardrails like RLHF (Reinforcement Learning from Human Feedback) that prevent them from processing or generating exploit code. This artificially blinds defenders, preventing them from using AI to simulate advanced attacks, test vulnerabilities, or understand the exact methods threat actors are actively using.

Fragmented tool parsing often leads to malformed or hallucinated API calls. This is prevented by standardizing the tool-calling datasets to perfectly match the base foundation model's formatting, which eliminates parsing failures and enables seamless agentic workflows.

MoE greatly improves model throughput. By routing tokens only to necessary experts, production data shows that end-to-end response times can drop by as much as half compared to running traditional dense models.

Yes. Because MoE decouples total parameter count from active compute, it achieves much higher efficiency. For example, Kindo's DeepHat v2 model used MoE to increase its context window by 8x (from 32,000 to 256,000 tokens) on the exact same hardware footprint as its predecessor.

Agentic AI autonomously calls external tools, but the output size of those tools is highly unpredictable (e.g., a tool might return a single line or a 150,000-token JSON log file). Without massive context windows (250k+ tokens), the AI will crash or suffer 'context-exceeded' errors when receiving large data returns.

Because MoE only activates a small fraction of its parameters at any given time, it drastically reduces the active memory footprint required for computation. This frees up vast amounts of KV cache memory, which can then be reallocated to support exponentially larger context windows.

Dense models activate every parameter for every token, which requires massive compute. This leads to severe memory bottlenecks in the Key-Value (KV) cache, restricting context windows and causing automated workflows to break when analyzing large datasets like SIEM logs or codebases.

A Mixture of Experts (MoE) is an AI architecture where the dense feed-forward layer of a transformer is replaced by a gating mechanism. Instead of activating the entire network for every task, the gating mechanism routes input tokens to only a small subset of specialized 'expert' neural networks, minimizing compute while maximizing knowledge.

After completing a successful investigation in chat mode, you can use the 'Save as Agent' feature. Kindo analyzes your entire conversation and distills it into an automated template with configurable inputs. For best results, maintain a human-in-the-loop to curate the auto-generated prompts, attach relevant reference materials to the Knowledge Store, and assign the optimal model for the workflow.

Agents can run autonomously in the background based on specific platform triggers, such as webhooks or scheduled times. For example, you can configure a workflow where a new Jira ticket containing the phrase 'vulnerability scan' automatically wakes the agent up to execute a predefined remediation runbook.

Kindo uses the Model Context Protocol (MCP) as its foundation, allowing for limitless tool connections. You can integrate custom, homegrown, or on-premise enterprise tools like SAP by connecting your own MCP servers. Additionally, Kindo can execute direct API calls from the shell for rapid, one-off integrations.

The Kindo Sandbox provides an isolated Kali Linux virtual machine directly accessible to the LLM. It acts as a secure environment where the AI can autonomously write code, execute nmap scans, clone repositories, and process massive files incrementally without exceeding its token context window.

No, Kindo is model-agnostic. You have access to all major commercial models, as well as open-source and uncensored models ideal for specialized tasks like red teaming. Administrators retain full control to route specific workloads to trusted models based on internal security and Data Loss Prevention (DLP) policies.

Yes. While Kindo offers a fully managed cloud SaaS platform, you can also deploy the entire Kindo stack within your own on-premise environment. We also support a hybrid deployment model, where the cloud platform securely connects to your local Model Context Protocol (MCP) servers.

Kindo ensures security through fine-grained tool policies, allowing you to dictate exact AI permissions on a step-by-step basis. API keys and sensitive credentials are encrypted in a secure Secrets Vault and never appear in audit logs or chat sessions. Additionally, every action is permanently recorded in comprehensive audit logs for human verification.

While in-app Copilots are isolated to assisting users within a single software application, IT and security professionals operate across multiple complex systems. Kindo provides a cross-tool, agentic solution that seamlessly hops between different applications to orchestrate end-to-end workflows.

Traditional SOAR vendors require tedious setup and rely on brittle integrations that easily break during API changes. Kindo offers a resilient, flexible alternative powered by natural language. This allows security and IT teams to conduct fluid incident investigations without the rigid constraints of legacy SOAR tools.

No. Kindo is designed to bring the power of AI directly to your existing ITOps and SecOps teams. Through its 'Chat Actions' and natural language interface, any engineer who can describe their objective can leverage Kindo without needing an internal AI center of excellence.

Kindo can be deployed as a fully managed SaaS, entirely on-premise for air-gapped networks, or in a hybrid model. The hybrid approach uses local Model Context Protocol (MCP) servers to securely connect the AI to restricted internal systems without exposing them to the public internet.

Kindo employs an Enterprise Secrets Vault coupled with Data Loss Prevention (DLP) filters. This ensures that sensitive credentials, API keys, and proprietary code are securely managed and never mishandled or leaked to external LLM providers.

Yes. Kindo utilizes a Secure Sandbox—an isolated Linux VM—where AI agents can safely execute Python scripts, run network mappers like nmap, or execute curl commands without risking the integrity of the broader enterprise network.

Kindo features strict, fine-grained Role-Based Access Controls (RBAC) and human-in-the-loop approvals. An AI agent can stage complex actions—like drafting a GitHub Pull Request or preparing a CrowdStrike policy—but a human operator must provide final authorization via tools like Slack before execution.

Traditional SOAR platforms require engineers to build complex, drag-and-drop workflows that are often brittle and break when APIs change. Kindo uses fully agentic AI, allowing users to trigger cross-platform automations using natural language without needing to build or maintain rigid visual logic trees.

Traditional penetration testing relies on highly paid human experts and is typically conducted infrequently due to high costs. Autonomous AI agents can run continuous, 24/7 security assessments for a fraction of the computing cost, significantly reducing the financial burden while drastically improving real-time threat detection.

Yes. As demonstrated by the Kindo Red Teaming Demonstration, an autonomous agent can start with simple reconnaissance (like a curl command), discover hidden variables, extract database credentials, and immediately pivot to executing a targeted SQL injection based on the newly discovered information.

It is an autonomous cycle managed by the AI agent consisting of Reconnaissance, Vulnerability Identification, Exploitation, and Reporting. Once given a high-level prompt, the AI continuously executes command-line tools in a sandbox to discover and exploit network vulnerabilities without human intervention.

The scratchpad concept refers to the AI using its execution sandbox to process massive amounts of data—like gigabytes of log files—that would normally exceed its token context window. Instead of reading the whole file, the AI executes filtering commands (like grep or awk) in the sandbox and only reads the relevant output anomalies into its memory.

The AI agent interacts with the sandbox via tool-calling APIs. It can write bash scripts or commands, push them to the Kali Linux environment to execute, and then read the terminal output back into its context window. This allows it to run real security tools like Nmap, curl, and Metasploit autonomously.

An uncensored AI model is a large language model that has had safety guardrails and artificial refusal mechanisms removed or minimized. This allows the model to freely discuss, analyze, and generate code related to offensive cybersecurity tactics without moralizing or blocking the user's prompt.

Standard commercial LLMs are trained with strict Reinforcement Learning from Human Feedback (RLHF) guardrails designed to prevent the generation of malicious content. Consequently, they routinely refuse requests to write exploits, generate reverse shells, or analyze vulnerabilities, making them ineffective for offensive security operations.

Enterprise AI requires highly granular, task-by-task RBAC. Rather than simply granting a user blanket access to an AI model, the system must control exactly which integrations, internal tools, and specific models an AI agent is authorized to use for a distinct action, strictly limiting the agent's operational scope.

DLP filters act as a secure gatekeeper between the user (or AI agent) and the LLM endpoint. They use techniques like Regular Expressions (Regex) and phrase matching to instantly identify, block, or sanitize sensitive data (like PII, financial details, or proprietary code) before the prompt is sent to an external commercial model.

Model Agility is the architectural capability to dynamically route prompts and data through different types of AI models based on the task's requirements and data sensitivity. It allows an enterprise to seamlessly switch between commercial models, self-hosted open-source models, or specialized models to balance cost, performance, and trust.

Secrets Vaults provide an isolated, encrypted environment to store sensitive API keys and bearer tokens. Instead of exposing these credentials in chat interfaces, environment variables, or audit logs, the AI orchestration layer dynamically injects them into API calls at the moment of execution, ensuring they remain hidden from both users and the LLMs.

The default open-source community implementation of MCP assumes local credential storage, which works for individual developers but is highly insecure in a scalable, multi-tenant cloud environment. Storing credentials locally in the cloud exposes backend infrastructure and violates zero-trust security principles, necessitating a custom fleet of dedicated MCP servers.

The Model Context Protocol (MCP) is a standard introduced by Anthropic designed to unify how AI tools and models communicate. It acts as a universal bridge, allowing AI agents to seamlessly connect to external data sources, execute tools, and interact with disparate systems in a standardized way.

Yes. The AI agent natively understands Terraform state, module inputs, and provider configurations. It can rewrite vulnerable HCL code, commit the changes, and open a formal Pull Request that resolves the misconfiguration.

AI agents are triggered automatically by webhook events. For example, the creation of a new Jira or Linear ticket containing an Nmap scan or a CrowdStrike alert will instantly wake up the agent to begin its investigation and remediation pipeline.

The AI agent uses API integrations to query CrowdStrike automatically when a malware alert triggers via Jira. It pulls device details, network history, and process execution trees to compile a deep security analysis report, and can proactively stage new malware protection policies within CrowdStrike for human review.

No. Following the Human-in-the-Loop model, the AI agent is only granted permissions to create branches and draft Pull Requests. It does not have the authority to merge code into production branches without human approval.

The AI agent ingests the Nmap scan details, cross-references identified vulnerabilities with the National Vulnerability Database (NVD), and analyzes the structural dependencies in private GitHub repositories to logically map exposed runtime infrastructure back to the exact lines of Infrastructure-as-Code (like Terraform) that provisioned it.

Human-in-the-Loop Remediation is an operational model where AI handles the tedious steps of discovery, correlation, and drafting fixes (like creating a GitHub PR or staging an EDR policy), while human operators act solely as the final approver for code merges and policy changes to ensure safety and accuracy.

By eliminating the manual toil of logging into multiple systems and copy-pasting context, AI agents can autonomously gather intelligence, correlate logs, and execute remediation actions in seconds. This allows human engineers to make immediate decisions based on synthesized data, drastically lowering MTTR.

No. In fact, it requires less coding and specialized platform knowledge than traditional SOAR tools. Because the primary interface is natural language, engineers can orchestrate complex, cross-platform workflows simply by typing clear instructions, drastically reducing the learning curve.

Instead of manually mapping out complex logic trees in a visual builder, engineers simply describe their automation goals in natural language. The AI agent acts as a reasoning engine, automatically determining the sequence of tools to call, the APIs to query, and the logic required to achieve the stated objective.

Bimodal AI Operations is an architectural framework that divides AI tasks into two modalities: an interactive Chat modality for ad-hoc, unpredictable investigations, and an Autonomous Agent modality that runs scheduled, trigger-based runbooks in the background to handle routine operational tasks.

App-specific Copilots are siloed within a single application's ecosystem. Real-world IT operations and security investigations require cross-referencing data across multiple platforms (e.g., Okta, AWS, Datadog, Jira). A localized Copilot cannot orchestrate actions across these disconnected tools.

Traditional SOAR platforms rely on deterministic, hardcoded logic and exact API payload matches. When an API changes or an unexpected edge case occurs, the rigid workflow breaks. AI agents, however, use probabilistic reasoning (LLMs) to dynamically interpret new data schemas and adapt to changes on the fly without breaking.

Yes. Upon completing a workflow, the Kindo agent can automatically compile and distribute an executive summary to communication channels like Slack. These summaries detail the initial malware alert analysis, the specific remediation actions taken, and tailored security recommendations for the team.

Kindo agents are engineered for enterprise-grade resilience. If an agent encounters an issue, error, or unexpected response from a connected service, it does not immediately fail. Instead, it dynamically analyzes the obstacle and intelligently attempts to find a logical workaround to ensure the successful completion of the workflow.

Yes. The Kindo platform provides full, transparent visibility into every agent execution. Security and compliance teams can open the run details to inspect the granular inputs and outputs of every API call made to connected services, ensuring complete accountability over gathered data and executed actions.

Kindo seamlessly integrates with a wide ecosystem of enterprise IT and security tools to facilitate comprehensive orchestration. This includes ticketing and alerting systems like Jira and Splunk, endpoint protection platforms (EPP) like CrowdStrike, and team communication platforms like Slack.

No custom coding or scripting is required. Kindo agents are 100% driven by natural language prompting. You can upload your existing incident response runbooks, connect your enterprise services, and the agent will follow your human-readable instructions to execute complex security workflows.

Unlike traditional SOAR platforms that require rigid, hard-coded scripting to execute API calls, Kindo uses AI agents driven entirely by natural language. Security teams simply provide a human-written playbook, and the Kindo agent autonomously translates those instructions into the correct dynamic service calls, drastically reducing setup time and maintenance overhead.

The Kindo agent orchestrates end-to-end incident response using natural language playbooks. When an alerting system like Splunk creates a ticket in Jira, it triggers the Kindo agent via a webhook. The agent autonomously queries CrowdStrike for device, login, and network telemetry, analyzes the threat, updates prevention policies for immediate remediation, and posts a comprehensive executive summary to Slack.

During a single demonstration run, the Kindo AI agent successfully analyzed and quarantined 14 separate malware files. Additionally, the entire workflow was achieved using 100% natural language playbooks, executing immediately upon the webhook trigger.

The demonstration highlighted seamless integrations with Jira (for ticketing and alerting), CrowdStrike (for endpoint protection, device details, and log analysis), and Slack (for automated team communication and executive summaries).

The Kindo agent features built-in error resiliency. If it hits an issue or an unexpected roadblock while executing a playbook, it will intelligently try to find a workaround to complete the necessary security tasks.

No. According to Kindo's CTO, Brian Van, the workflows are 100% driven by natural language prompting. There is absolutely no hard-coded scripting required, making it accessible to analysts without advanced coding backgrounds.

The Kindo agent listens for Jira alerts via webhook. Upon triggering, it executes a natural-language runbook to autonomously query CrowdStrike for deep analysis, enable required security policies, and post an executive summary of the incident directly to Slack.

Kindo addresses the time-consuming, manual execution of incident response runbooks. Traditionally, analysts must manually cross-reference tools like Jira, CrowdStrike, and Slack to investigate alerts and update policies, leading to delayed response times and alert fatigue.

During the demo, the Kindo AI agent analyzed an alert involving 14 quarantined files. It autonomously identified the activity as legitimate security testing (rather than an actual breach), enabled a 'Phase 2' protection policy in CrowdStrike, and sent a complete summary to the team via Slack without any human intervention.

By eliminating the 'swivel-chair effect' where human analysts manually copy and paste data across multiple platforms. The AI handles all routine data gathering and triage, meaning human analysts stay out of the manual process and only receive an executive summary of completed remediation actions.

AI agents seamlessly bridge siloed enterprise tools. They typically ingest alerts from ITSM platforms (like Jira) or SIEMs, gather telemetry and enact policy via EDR platforms (like CrowdStrike), and deliver executive summaries to communication hubs (like Slack).

Yes, when properly architected. Modern AI agents use advanced contextual awareness to accurately differentiate between real threats and benign anomalies. By trusting AI with policy enforcement, organizations can achieve machine-speed containment (drastically lowering MTTR) to stop fast-moving threats like ransomware before they spread.

Traditional SOAR platforms rely on static, pre-programmed runbooks (IF/THEN logic) that still frequently require human approval for execution. AI agents utilize dynamic reasoning to actively investigate context, differentiate between threats (e.g., actual malware vs. penetration testing), and autonomously decide which policy changes to implement.

Zero-Touch Incident Response is a security framework where AI agents autonomously handle the entire lifecycle of an alert—from initial trigger and context gathering to decision making, production policy enforcement, and reporting—without requiring manual human intervention.

No. Advanced AI agents are designed to interface with standard REST APIs and tool ecosystems exactly as human analysts or traditional scripts would. The intelligence lies in the agent's ability to interpret human intent and dynamically route requests to the tools you already have deployed, such as CrowdStrike, Jira, or Slack.

The Natural Language Automation Paradigm drastically reduces engineering costs. Security engineers no longer have to spend weeks acting as 'API mechanics'—writing, updating, and maintaining brittle Python scripts every time a vendor changes a data schema. This frees up highly compensated talent to focus on advanced threat hunting and strategic security posture.

Absolutely. Complete transparency is a core feature of enterprise AI agents. Every action the agent takes, including input prompts and the exact output logs for every API call made, is recorded. Security teams can fully audit how the agent mapped the natural language playbook to specific technical executions.

Unlike traditional, hard-coded scripts that break entirely when an exception occurs, natural language AI agents feature self-healing capabilities. Because the agent understands the overarching goal of the playbook, it can actively seek workarounds, such as retrying a query, utilizing an alternate endpoint, or finding the required data from another integrated tool.

Yes. AI agents can autonomously navigate between multiple platforms. For example, in a live demonstration, an AI agent seamlessly connected Jira, CrowdStrike, and Slack, pulling complex alert details, device states, and network history, and then formatting the output for a messaging channel—all driven by a single natural language prompt with zero hard-coded scripts.

Traditional SOAR platforms require engineers to write complex code or build strict visual workflows to map out APIs and manage data transfers. Natural language playbooks allow security teams to write standard, plain-English runbooks. An AI agent interprets these runbooks, understands the intent, and autonomously maps the correct API calls to execute the playbook without any hard-coded logic.

Chat Actions are Kindo's overarching autonomous AI tools designed to interact directly with the sandbox. They empower the AI to transition from providing conversational responses to executing functional, secure operations—such as coding, troubleshooting, and infrastructure management—directly within your secure environment.