Quick facts
- Best for
- AI-powered external exposure assessment at scale.
- Pricing
- Freemium
- Editor rating
- 4.5 / 5
- Community saves
- 0
About CyCognito
AI at CyCognito is designed to assist with external exposure assessment on a large scale, validates the risk and directs remediation priorities. It leverages various AI technologies to offer a comprehensive solution for surveillance and risk management. This includes intelligent discovery, where AI models aid in establishing associations between assets to generate a continuously updated interpretation of your digital footprint. CyCognito uses semantic search aided by natural language processing that enables users to make data requests in plain language and receive useful responses. Their contextual analysis feature uses AI to identify an assets function, underlying technology, and exposure traits, providing insights about potential attackers' interest to create a risk profile for prioritization. Through adaptive testing, AI insights ensure tests are run comprehensively for each asset, adapting as the environment evolves for risk validation. In terms of security, they have automated security testing to focus on significant risks as the environment changes. Moreover, AI-assisted logic is employed for routing each finding to the appropriate owner, providing clear remediation context and confirming that fixes are complete. The AI technologies powering CyCognitos platform include Bayesian Machine Learning, Large Language Models, Generative AI models, Graph-based AI, and Natural Language Processing.
Pros
- External exposure assessment
- Validates risk at scale
- Intelligent discovery
- Updated digital footprint interpretation
- Semantic search feature
- Natural language data requests
- Contextual analysis
- Assets' function and exposure identification
- Risk profile creation
- Adaptive testing
- Environment-evolving risk validation
- Automated security testing
Cons
- Complex implementation process
- Possible high false positives
- Absence of real-time risk monitoring
- Risk classification may be subjective
- Dependent on natural language input
- Lack of third-party integrations
- Adaptability takes time
- Automated remediation context may be unclear