Enterprise organizations are generating more data than ever, but having large volumes of data does not automatically provide better security.
Security teams often work across disconnected tools, identities, applications, cloud environments, security controls, and data sources. This fragmentation can make it difficult to understand what assets exist, where security gaps are located, and which risks require immediate attention.
Against this backdrop, Prevalent AI has raised $22 million in growth funding from Integrity Growth Partners to expand its AI powered data fabric platform and accelerate its growth. The London based company, founded in 2017 by former GCHQ and Darktrace leaders, had previously operated without external funding.
The company has developed a platform designed to bring fragmented enterprise data together, transform it into a knowledge graph, and provide security teams and AI agents with greater context and visibility.
Why Enterprise Data Context Matters
Modern enterprises rarely operate from a single technology environment.
A typical organization may have:
• Cloud platforms
• On premises infrastructure
• SaaS applications
• Identity systems
• Security tools
• Databases
• APIs
• Endpoint technologies
• Third party services
• AI models and AI agents
• Compliance systems
When information from these environments remains isolated, security teams can struggle to build an accurate picture of organizational risk.
A data fabric approach aims to connect these fragmented sources and create relationships between them.
This can help security teams understand not only individual security events, but also how systems, identities, controls, assets, and risks are connected.
AI Is Increasing the Need for Better Data
The rapid adoption of AI is making data visibility even more important.
AI agents increasingly require access to enterprise information to perform tasks, answer questions, automate workflows, and make decisions.
However, giving an AI agent access to enterprise data without understanding the underlying relationships and permissions can create significant security risks.
Organizations need to know:
• What data is available
• Where sensitive data is stored
• Who can access it
• Which applications can access it
• Which AI agents can interact with it
• What security controls protect it
• Where compliance gaps exist
• Which risks require remediation
Without this context, AI automation can potentially increase the organization’s attack surface.
From Data Collection to Actionable Security Intelligence
Prevalent AI’s platform is designed to clean, connect, and contextualize enterprise security data while continuously identifying operational risks and gaps.
This approach highlights an important shift in cybersecurity.
Organizations do not simply need more security data. They need security intelligence that can be understood and acted upon.
For example, knowing that an application has a vulnerability provides useful information.
Knowing that the vulnerable application:
• Handles sensitive customer information
• Is exposed to the internet
• Has privileged access
• Connects to critical systems
• Is used by an AI agent
• Has inadequate security controls
provides much more meaningful risk context.
This is where connected security data can improve decision making.
Securing AI Agents Requires Context
As organizations move toward agentic AI, security teams must consider the permissions and relationships surrounding AI systems.
An AI agent may interact with:
• Enterprise databases
• Internal applications
• Cloud services
• APIs
• Identity platforms
• Business workflows
• Customer information
• Security systems
The more systems an agent can interact with, the more important it becomes to understand its identity, permissions, data access, and behavior.
AI security therefore cannot be separated from broader enterprise data security.
The Importance of Continuous Risk Identification
Traditional security assessments often provide a snapshot of an organization’s environment.
Enterprise environments, however, change continuously.
New applications are deployed. Employees change roles. Cloud resources are created. Vulnerabilities emerge. Access permissions change. AI applications are introduced. Third party integrations are added.
Continuous risk identification can help organizations detect these changes and respond more quickly.
A mature security program should continuously evaluate:
• Asset exposure
• Vulnerability status
• Identity and access privileges
• Security control effectiveness
• Sensitive data locations
• Third party connections
• Cloud configurations
• AI system access
• Compliance requirements
• Operational security gaps
Data Fabric and Compliance
Data visibility also plays an important role in compliance.
Organizations subject to regulations and security frameworks need to demonstrate that appropriate controls exist and that sensitive information is properly protected.
Connected security data can support compliance activities by helping organizations understand:
• Where regulated data exists
• Which systems process it
• Who has access
• Which controls protect it
• Where security gaps remain
• Whether remediation activities are complete
• How security controls relate to business systems
This can support broader governance efforts involving frameworks and regulations such as GDPR, HIPAA, PCI DSS, NIST, and ISO 27001.
Industries That Can Benefit
The need for connected security intelligence extends across many industries.
Financial Services
Banks, FinTech companies, insurance providers, and financial institutions manage highly sensitive financial and customer information.
COE Security can help these organizations assess data exposure, identity risks, cloud environments, AI applications, APIs, and security controls while supporting compliance initiatives.
Healthcare
Healthcare organizations operate complex environments containing sensitive patient and clinical information.
COE Security can help assess data security, AI applications, cloud infrastructure, application security, access controls, and compliance requirements.
Retail and E-Commerce
Retail organizations manage customer information, payment systems, e-commerce platforms, cloud services, and third party integrations.
COE Security can help identify security gaps across applications, APIs, cloud environments, data stores, and connected technologies.
Manufacturing
Manufacturing organizations increasingly combine traditional operational environments with cloud platforms, IoT, AI, and connected applications.
COE Security can help assess IT, OT, IoT, cloud, network, application, and AI security risks.
Government
Government organizations manage sensitive citizen information and critical infrastructure while increasingly adopting cloud and AI technologies.
COE Security can help strengthen data governance, identity security, application security, AI security, monitoring, and compliance programs.
Technology and SaaS
Technology providers and SaaS organizations often manage large and complex environments with extensive APIs, cloud infrastructure, customer data, and AI capabilities.
COE Security can help integrate security into the software development lifecycle while assessing applications, APIs, cloud infrastructure, AI systems, and third party dependencies.
What Organizations Should Do
The growth of AI powered data platforms highlights several priorities for security leaders.
Organizations should consider:
• Building a comprehensive enterprise asset inventory
• Connecting security and operational data sources
• Establishing clear data ownership
• Mapping sensitive data across environments
• Reviewing identity and access privileges
• Applying least privilege principles
• Continuously monitoring vulnerabilities
• Assessing third party and supply chain risks
• Monitoring AI agent access to enterprise systems
• Implementing AI governance controls
• Conducting regular penetration testing
• Integrating security into the SSDLC
• Maintaining detailed security and compliance records
• Continuously validating security controls
The Bigger Picture
Prevalent AI’s latest funding reflects a broader movement toward using AI and connected data to improve enterprise security operations.
The company plans to use the new investment to accelerate its expansion in the United States, grow its go to market operations, extend its platform beyond cybersecurity, and expand its leadership team.
The underlying challenge extends beyond any single technology platform.
As enterprise environments become more distributed and AI adoption accelerates, organizations need a reliable understanding of how their systems, data, identities, applications, controls, and AI agents are connected.
Better context can lead to better risk decisions.
Conclusion
Enterprise cybersecurity is increasingly becoming a data and context problem.
Security teams can have thousands of alerts, tools, identities, assets, and security controls, yet still struggle to understand which risks matter most.
The emergence of AI powered data fabric platforms demonstrates the growing importance of connecting fragmented enterprise information and turning it into actionable security intelligence.
As organizations deploy AI agents across critical business environments, data visibility, identity management, access control, continuous monitoring, and governance will become increasingly important.
Organizations that combine AI innovation with strong cybersecurity and compliance practices will be better positioned to adopt new technologies while protecting sensitive information and reducing operational risk.
About COE Security
COE Security partners with organizations in financial services, healthcare, retail, manufacturing, and government to secure AI-powered systems and ensure compliance.
Our offerings include:
• AI-enhanced threat detection and real-time monitoring
• Data governance aligned with GDPR, HIPAA, and PCI DSS
• Secure model validation to guard against adversarial attacks
• Customized training to embed AI security best practices
• Penetration Testing (Mobile, Web, AI, Product, IoT, Network & Cloud)
• Secure Software Development Consulting (SSDLC)
• Customized CyberSecurity Services
In addition, COE Security helps organizations strengthen enterprise security and data governance through AI security assessments, data security assessments, cloud security assessments, identity and access management reviews, API security testing, application security testing, vulnerability management, penetration testing, AI agent security assessments, third party risk assessments, software supply chain security assessments, DevSecOps consulting, compliance readiness, and secure software development programs.
For financial services organizations, we help protect sensitive financial data, assess AI and cloud environments, strengthen identity controls, and support cybersecurity and compliance requirements.
For healthcare organizations, we help protect sensitive information, evaluate AI applications, assess cloud and application security, and strengthen data governance.
For retail and e-commerce organizations, we help secure customer data, applications, APIs, cloud infrastructure, payment related environments, and third party integrations.
For manufacturing organizations, we help assess connected IT, OT, IoT, AI, cloud, network, and application environments.
For government organizations, we help strengthen data protection, identity security, AI governance, application security, monitoring, and compliance controls.
For technology and SaaS companies, we help secure applications, APIs, cloud infrastructure, AI systems, software development pipelines, and third party dependencies.
Our goal is to help organizations understand their security posture, identify risks proactively, protect critical data, secure AI adoption, and build resilient and compliant digital environments.
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