Overview

Product details compiled from public sources, each with a citation.

Vendor: Knostic2
Description: Security for AI coding assistants such as Cursor, Copilot, and Claude Code that inspects MCP connections in real time, monitors IDE extensions and plugins, and blocks risky components.2
Deployment: SaaS1
Status: Active1

Matrix Coverage

Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.

Asset class	Identify	Protect	Detect	Coverage	Source
AI Orchestration Tools	Identify: Covered	Protect: Covered	Detect: Covered	Primary	2
AI-Generated Code	Identify: Not covered	Protect: Covered	Detect: Covered	Secondary	2
Runtime AI Data	Identify: Not covered	Protect: Covered	Detect: Not covered	Secondary	1

Framework Relevance

These frameworks include controls relevant to the asset classes Knostic Kirin defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Knostic implements these controls or is certified against them.

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Framework	Asset class	Relevant controls
NIST IR 8596	AI Orchestration Tools	Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates
NIST IR 8596	Runtime AI Data	Prompts (runtime); inference data
CSA AI Controls Matrix	AI Orchestration Tools	Application and Interface Security; Supply Chain Management
	AI-Generated Code	Application and Interface Security; Supply Chain Management
	Runtime AI Data	Data Security and Privacy Lifecycle Management; Application and Interface Security
ISO 42001	AI Orchestration Tools	A.6 AI system life cycle; A.5 Assessing impacts of AI systems
	AI-Generated Code	A.6 AI system life cycle
	Runtime AI Data	A.7 Data for AI systems; A.8 Information for interested parties
Google SAIF	AI Orchestration Tools	Secure the AI supply chain; application and pipeline security; agent orchestration controls
	AI-Generated Code	Secure the AI pipeline; code provenance and supply chain integrity
	Runtime AI Data	Expand AI red-teaming; runtime input and output safety; prompt defense
SANS Critical AI Security Guidelines	AI Orchestration Tools	Secure Agentic Systems and AI Autonomy Controls (defined function scope; execution isolation; API and function-call gating); Limit Model Behavior (focused functionality; access controls outside the model)
	AI-Generated Code	Model I/O Handling (AI deployment in IDEs: prefer local-only integrations to limit exposure of code, keys, and proprietary data); Governance, Risk, Compliance (regularly test and red-team AI applications before and after deployment)
	Runtime AI Data	Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails)
MITRE ATLAS	AI Orchestration Tools	AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins)
	AI-Generated Code	AML.T0010 AI Supply Chain Compromise (hallucinated dependencies and slopsquatting); AML.T0018 Manipulate AI Model (when models embed code-execution backdoors)
	Runtime AI Data	AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt
OWASP AI Exchange	AI Orchestration Tools	Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools
	AI-Generated Code	Development-time threats: insecure code generation, license risk, hallucinated dependencies
	Runtime AI Data	Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering
OWASP LLM Top 10	AI Orchestration Tools	LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption
	AI-Generated Code	LLM06 Excessive Agency (code execution); insecure or vulnerable code patterns inherited from training data
	Runtime AI Data	LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling
OWASP Agentic Security Top 10	AI Orchestration Tools	ASI01 Agent Goal Hijack; ASI02 Tool Misuse and Exploitation; ASI05 Unexpected Code Execution (RCE); ASI07 Insecure Inter-Agent Communication; ASI08 Cascading Failures; ASI10 Rogue Agents
	AI-Generated Code	ASI05 Unexpected Code Execution (RCE); ASI04 Agentic Supply Chain Vulnerabilities (hallucinated dependencies and vibe-coding artifacts)
	Runtime AI Data	ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs)

Provenance

Last sourced 2026-06-11.

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Sources

Kirin | Security Platform for AI Coding Assistants
Vendor source accessed 2026-06-11
AI Coding Security Solution | Kirin by Knostic
Vendor source accessed 2026-06-11

Changelog

2026-06-11
Corrected the entry to describe Kirin, the Knostic product that secures AI coding assistants. The previous text described the separate Knostic knowledge-security platform.
2026-06-07
Verified details and sources; set the product name to Knostic Kirin (company Knostic) and corrected the URL and category.

Found an error? Corrections are welcome. Suggest an edit.

Product Strategy and Positioning

You can use the following frameworks to understand the product’s strategy and its competitive positioning. Performing this analysis is outside the scope of the AI Defense Matrix Catalog, but the following guidance can help you with such an assessment.

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Product Strategy

Lenny Zeltser’s Guide to Creating Cybersecurity Products can help you understand key aspects of the product strategy. You can use your AI tool to gather the data and apply this framework.

Market segment: Who the product is built for: industry, size, and the persona who evaluates it.
Go-to-market motion: How it reaches buyers: top-down sales, bottom-up adoption, or open source.
Pricing model: How value is captured: per-seat, consumption, or outcome-based.
Delivery and operations: How it is deployed, configured, and maintained, including infrastructure-as-code and API coverage.
Customer trust: Certifications, transparency, and supply-chain security a buyer expects from the vendor.
Ecosystem position: A point solution, a platform others build on, or a component of a larger platform.

Strategy Defensibility

Ben Vierck’s rubric can help you assess the defensibility of the SaaS product’s strategy against competitive and other market forces. You can use it with your AI tool for a methodical analysis.

Value delivery: How much of the value is hard to replicate versus standard software a competitor could rebuild.
Switching cost: How costly it is to leave once deployed: integrations, data, workflow, and platform ties.
Compliance moat: Whether certifications or regulatory alignment are a durable advantage or table stakes for this buyer.
Problem complexity: How hard, adversarial, and fast-moving the underlying problem is to solve well.
Buyer profile: Who holds the budget, and how durable that demand is across the market.
Layer: Where the product operates: application, model, infrastructure, platform, or identity control plane.
Proprietary data, content, or IP: Whether it accumulates data, content, or IP that others would find difficult to replicate.