Data security platform Matters.AI has integrated with cloud and AI security company Upwind, introducing a native API connection designed to help enterprise security teams identify which cloud exposures may put sensitive business data at risk.
The integration combines Upwind’s cloud security context with Matters.AI’s data discovery, classification and Database Activity Monitoring capabilities, bringing infrastructure risk and sensitive-data intelligence into a single investigative view.
The development is aimed at reducing the need for cybersecurity teams to manually correlate findings across separate cloud and data security systems.
Matters.AI and Upwind Bring Cloud and Data Risk Into One View
A critical cloud security alert may indicate a vulnerable workload, exposed service or misconfiguration, but it does not necessarily explain what business data could be affected.
Upwind provides runtime context around workloads, processes, identities and network paths, while Matters.AI adds information about the data associated with those environments, including its sensitivity, access permissions and activity.
By combining these two layers, the companies aim to help security teams understand the relationship between an infrastructure exposure and the sensitive information potentially affected by it.
This approach reflects a broader shift in enterprise cybersecurity towards prioritising threats based not only on technical severity, but also on business impact.
For more information on Matters.AI’s approach to data risk prioritisation, visit <a href=”https://www.matters.ai/use-case/dynamic-risk-prioritization” target=”_blank” rel=”noopener noreferrer”>Matters.AI’s Dynamic Risk Prioritization platform</a>.
How the Matters.AI-Upwind Integration Works
Under the integration, Matters.AI maps Upwind security findings against its own classification output for affected data stores.
For posture and configuration findings, Matters.AI uses a 24-hour ingestion cycle.
Where Upwind acts as the authoritative posture source for an account, Matters.AI suppresses its own posture findings to reduce duplication. This allows security teams to work from an exposure queue that takes both cloud context and data sensitivity into account.
Matters.AI’s Database Activity Monitoring, or DAM, capabilities add query-level information to the investigation process, giving security teams greater visibility into how databases are being accessed and used.
More details on the company’s Database Activity Monitoring technology are available through Matters.AI’s official DAM platform page
Runtime Cloud Visibility Meets Sensitive Data Intelligence
Harsh Sahu, Co-Founder and CTO at Matters.AI, said the integration strengthens the connection between real-time cloud security intelligence and sensitive-data protection.
“Integrating Matters.AI with Upwind’s runtime visibility is a huge leap forward for data security. By enriching our AI-native semantic engine with live process and socket telemetry, Matters.AI gives security teams a complete, single-pane view of data risk, linking real-time cloud execution directly back to the sensitive assets we protect.”
The combination of runtime telemetry and semantic data classification could help security teams assess the potential business impact of an exposure more rapidly.
Rather than examining infrastructure vulnerabilities and data risk separately, responders can analyse them together as part of the same investigation.
Upwind Adds Real-Time Cloud and AI Security Context
Alon Saban, Head of Tech Alliances at Upwind Security, said Upwind provides real-time runtime intelligence enriched with broader cloud and AI security context.
“Matters.AI leverages this runtime intelligence to broaden and enrich its data sets, connecting what’s happening in the cloud with the sensitive data at risk. Together, we give customers a more complete picture of risk and help them prioritize what matters most.”
Upwind’s platform combines agentless scanning with runtime sensors and provides security capabilities spanning cloud security posture management, vulnerability management, attack surface management, API security, data security, AI security and real-time threat detection.
Native API Integration Reduces Duplicate Cloud Scanning
Customers can connect their existing Upwind organisation to Matters.AI through the native API connector.
Matters.AI then uses cloud posture information already collected by Upwind, helping organisations avoid onboarding the same cloud accounts into another posture scanner.
The architecture also separates sensitive-data processing from operational metadata exchange.
Matters.AI’s scanning and classification takes place within the customer’s own environment, while operational metadata is exchanged through API connections.
The resulting view is designed to combine:
- Cloud infrastructure findings
- Sensitive-data classification
- Access permissions
- Runtime activity
- Database query information
- Security and compliance evidence
For organisations operating across complex cloud environments, bringing these signals together could help reduce investigation time and improve incident prioritisation.
Why Data Context Matters in Cloud Security
One of the biggest challenges in modern cybersecurity is alert prioritisation.
Security teams can receive large numbers of alerts across cloud infrastructure, applications, APIs, databases and identity systems. However, technical severity alone does not necessarily indicate actual business risk.
A vulnerability connected to a system containing non-sensitive test data may carry a different business impact than the same vulnerability affecting customer records, financial information or intellectual property.
This is where data classification becomes increasingly important.
Matters.AI positions its platform around contextual risk analysis, combining factors such as data sensitivity, permissions, user behaviour and external exposure to determine which risks require more urgent attention.
AI Security Is Expanding the Enterprise Attack Surface
The integration also comes at a time when organisations are rapidly deploying AI applications and autonomous systems across enterprise environments.
As AI tools gain access to corporate databases, internal documents and cloud infrastructure, security teams are increasingly being required to understand not only who can access sensitive information, but also which AI systems and machine identities can interact with it.
Matters.AI has been expanding its security capabilities around this challenge.
Its AI risk platform is designed to identify AI assets and map their access to sensitive enterprise information.
The company says the technology can help organisations identify shadow AI usage, autonomous machine identities and situations where AI applications have excessive access to corporate data.
More information is available on Matters.AI
Matters.AI Builds AI-Native Data Security Platform
Matters.AI describes itself as an AI-native data security platform designed to function like an AI Security Engineer.
Its platform brings together multiple security capabilities, including:
- Data Security Posture Management
- Data Detection and Response
- Insider Risk
- Exfiltration Defence
- Data Loss Prevention
- Database Activity Monitoring
- AI risk intelligence
The company’s technology focuses on understanding what sensitive information exists inside an organisation, who or what is accessing it, and how that data is being used.
Matters.AI’s database security platform also focuses on monitoring interactions involving human users, machine identities and AI agents, with capabilities covering semantic classification and real-time data lineage. Matters.AI
The company says it currently protects more than 50 enterprises, monitoring petabytes of data and billions of events every day.
Matters.AI is backed by Better, CVP, Kalari and Endiya.
Upwind Focuses on Runtime Cloud and AI Security
Upwind is a cloud and AI security company founded by the team behind Spot.io.
Its platform combines agentless scanning with runtime sensors to provide security, development and DevOps teams with visibility into cloud and AI infrastructure.
The company focuses on helping organisations identify high-signal security risks, prioritise remediation and respond to threats as they occur.
Its technology covers areas including cloud security posture management, vulnerability management, API security, attack surface management, data security, AI security and runtime threat detection.
Upwind is headquartered in the United States and is led by Co-Founder and CEO Amiram Shachar.
What the Matters.AI-Upwind Integration Means for Enterprises
The integration reflects an important evolution in enterprise cybersecurity: the convergence of cloud security, runtime intelligence and data security.
Historically, organisations have often managed these areas through separate security tools.
Cloud security platforms may identify infrastructure vulnerabilities, while data security systems classify sensitive information and database monitoring products track user activity.
Bringing these signals together could allow security teams to move from alert-based security towards more contextual, risk-based decision-making.
For enterprise security teams, the integration is designed to help answer three important questions:
- What cloud exposure has occurred?
- What sensitive data could be affected?
- What activity is taking place around that data?
That additional context can be especially valuable for large enterprises operating across hybrid cloud environments, AI workloads and distributed application infrastructure.