Overview
- Eliminate unknown threats in your browser extensions by uncovering hidden behavioral patterns and supply-chain vulnerabilities that permissions alone miss, powered by Koi's LLM-first code analysis engine.
- Make data-driven endpoint security decisions with extensive publisher profiles, breach intelligence, and compliance data from continuous marketplace scans.
- Stop relying on declared functionality by automatically comparing it against actual code behavior to detect discrepancies that signal risk, with Koi's Wings risk engine.
- Reduce marketplace risks proactively with a preventive policy module that tracks, governs, and enables software installs across all endpoints based on real-time risk scores.
- Stay ahead of evolving threats as Koi dynamically updates risk scores with every new software version or change detected in marketplaces and app stores.
- Validate publisher trustworthiness instantly through cross-marketplace reputation analysis, geographical region data, and open-source knowledge aggregated by AI agents.
Pros & Cons
Pros
- Comprehensive risk analysis
- Browser extension security
- Identifies hidden behavioral patterns
- Detects supply-chain vulnerabilities
- Evaluates security implications
- Preventive policy module
- 'Wings' risk engine
- Assesses endpoint software
- Detailed functionality insights
- Scans software marketplaces
- Checks app stores and registries
- Updates on new software
- Examines declared and actual functionality
- Updates risk scores
- Beneficial for IT, GRC, SOC teams
- Extensive software data
- Publisher profile analysis
- Identifies applied vulnerabilities
- Provides breach intelligence
- Generates compliance data
- Supports data-driven decisions
- Enables endpoint security
- LLM-first risk engine
- Continuous marketplace scanning
- Comparative code analysis
- Publisher intelligence
- Dynamic analysis on actual code
- Software change responsive
- Connected to outside world
- Provides software categories, history
- Populates vulnerabilities, capabilities insights
- License and compliance information
Cons
- No mobile application support
- Only browser extension focused
- Does not support non-browser software
- Limited information on data privacy
- Lacks real-time risk notifications
- No multilingual support
- No indication of integration capabilities
- No offline mode available
- Inadequate publisher profile analysis
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❓ Frequently Asked Questions
Koi is a comprehensive risk analysis tool specializing in browser extension security. It utilizes AI to explore, analyze, and understand the code of browser extensions, aiming to identify behavioral patterns, supply-chain vulnerabilities, and security implications beyond the permission sets.
Key features of Koi include the 'Wings' LLM-first risk engine, proactive scanning of software marketplaces, app stores, and registries, analysis of actual code behind software, comparison between declared and actual software functionality, risk scoring, and facilitating data-driven decisions by providing extensive data about the software under inspection.
'Wings' is Koi's LLM-first risk engine feature. It reads the code of non-binary software on the endpoint, providing information on what the software was actually programmed to do. It proactively scans marketplaces, app stores, and registries for new software, assesses the publisher based on their online presence and cross-marketplace reputation, compares the actual software with its declared functionality and assigns a risk score based on detected indicators.
Security professionals, IT, GRC (Governance, Risk and Compliance), and SOC (Security Operations Centre) teams can benefit from using Koi. These teams often require critical risk data in their operations and Koi's extensive data about the software contributes key information for decision-making on endpoint security.
Koi's preventive policy module minimizes marketplace risks by tracking, governing, and enabling software installs across all endpoints. It aids in the discovery and governance of all software, both binary and non-binary.
Koi's AI analyzes a software's code through a combination of AI and LLMs (Low-Level Matchers). It reads and examines the software's code, providing insights into its intended functionality.
Koi provides extensive details about the software it evaluates including information about its functionality, the publisher, and composition. It also identifies vulnerabilities, presents breach intelligence and gives compliance data. Basically, it provides all crucial risk data needed for security, IT, GRC, or SOC teams.
Koi's risk scoring capability involves assigning a risk score to software based on detected indicators. These scores are dynamic and updated with every new software version or change detected.
By providing extensive and specific data about the software, including its functionalities, the publisher profile, and breach intelligence, Koi supports data-driven decision making. The risk score assigned to the software, reflecting the measured risk, further aids this purpose.
Koi aids in endpoint security by analyzing and providing insights on endpoint software. By leveraging it's 'Wings' feature, it proactively scans for new software in marketplaces, app stores, and registries, assesses the software, and provides a risk score based on detected vulnerability indicators.
Koi can identify a wide range of vulnerabilities in software codes, from hidden behavioral patterns to supply-chain vulnerabilities that could pose security risks beyond the software permissions.
Koi's breach intelligence feature works by researching software online. The AI agents search for pertinent information such as categories, history, known vulnerabilities, capability insights to enrich the software data, aiding in governance using actionable data.
Koi provides compliance information in terms of license & compliance data associated with software. This helps teams understand regulatory compliance attached to the software.
Koi regularly scans software marketplaces, app stores, and registries to fetch up-to-date information on new software. However, the exact frequency of these scans isn't specified on their website.
Declared functionality is what the software is presented or marketed as being able to do. Actual functionality, as examined by Koi, is about what the software is programmed and can actually do as revealed by its actual code. Koi serves to identify discrepancies between these two, indicative of possible risks or malintent.
Koi's software inspection method involves the use of AI, and LLMs to critically examine the software at the code level. It not just assesses the declared functionality, but delves deeper to understand the actual code behind the software. Moreover, it factors in the software's runtime behavior, captures network and endpoint activity, contributing further to the risk assessment.
Koi's publisher profile analysis involves assessing the publisher based on their online presence, geographical region, reputation across multiple marketplaces, and open-source knowledge. It provides a comprehensive view of the publisher, thereby aiding risk assessment.
Koi aids in risk assessment of browser extension security by giving detailed insights about the browser extension, including functionality, the publisher, and composition. It analyses the actual code, identifies potential risk indicators, assigns a risk score, and updates these scores with changes and new versions of the extension.
To uncover hidden risks in browser extension codes, Koi comprehensively analyses the code behind extensions. It not just considers the declared functionality, but scrutinizes the actual code to identify hidden behavioral patterns, supply-chain vulnerabilities, and broader security implications.
Supply-chain vulnerabilities refer to potential risks that occur within the extensions' code, its dependant libraries or software components it relies on. These vulnerabilities can result in security breaches and unauthorized accesses. Koi identifies such vulnerabilities by analyzing the browser extension's code thoroughly, and running comparisons between the promised functionality and the actual functionality as indicated by the code itself.
Pricing
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