Data in transit is information moving across a network between systems, users, or applications. Because it is exposed while travelling, it is more vulnerable to interception and tampering than data at rest. Protecting it usually requires encryption, authentication, and integrity controls that preserve confidentiality during transfer.
Expanded Definition
Data in transit refers to information actively moving across a network path, including traffic between user devices, servers, APIs, cloud services, and application components. In security practice, the term covers more than simple internet traffic. It also includes east-west movement inside internal environments, service-to-service calls, synchronisation between distributed systems, and communication across encrypted tunnels or private links. The core security concern is that data can be intercepted, altered, replayed, or redirected before it reaches its destination.
For NHI Management Group, the distinction matters because transport protection is not the same as endpoint protection or storage protection. Encryption helps preserve confidentiality, while integrity checks, mutual authentication, and session validation help confirm that the message has not been changed and that the recipient is legitimate. NIST guidance on network and communications protections, including NIST SP 800-53 Rev 5 Security and Privacy Controls, is often used to anchor these controls in a broader governance program. The most common misapplication is assuming that encrypted traffic is automatically safe, which occurs when teams ignore endpoint trust, certificate validation, or traffic inspection gaps.
Examples and Use Cases
Implementing data-in-transit protection rigorously often introduces latency, certificate management overhead, and operational complexity, requiring organisations to weigh stronger assurance against simpler connectivity.
- API traffic between microservices is protected with TLS and service identity checks so that requests cannot be read or modified in flight.
- Remote workforce access uses a VPN or zero trust access layer to secure data moving between endpoints and enterprise systems.
- Payment or customer-record traffic is encrypted during transmission to reduce the impact of interception on public or shared networks.
- Agent-to-tool communication in agentic AI systems is monitored and authenticated so that prompts, outputs, and tool calls are not tampered with during exchange.
- File transfers between partners use integrity validation and authenticated channels to confirm that the received file matches the original source.
These use cases are not interchangeable. A file transfer control may not be sufficient for high-frequency API calls, and a basic encrypted tunnel may not satisfy identity assurance needs where systems exchange sensitive credentials or tokens. Organisations often rely on transport security standards to decide where confidentiality, authenticity, and integrity controls should be enforced, but they still need to match the mechanism to the communication pattern.
Why It Matters for Security Teams
Security teams treat data in transit as a control boundary because compromise at this stage can expose credentials, session tokens, personal data, and operational commands even when databases remain protected. In practice, weak transport controls can undermine broader programs such as IAM, PAM, and Non-Human Identity governance, especially where service accounts, workload identities, and automation agents exchange secrets or signed requests across networks. That makes transport security a foundational requirement for zero trust and for any environment that depends on distributed services, cloud APIs, or remote access.
Misunderstanding this term often leads to blind spots in monitoring and policy design. For example, teams may protect storage well but leave internal service traffic unsigned, unauthenticated, or broadly trusted once it enters a private network. In agentic AI environments, this becomes especially important because tool calls, context payloads, and identity assertions can all travel between systems. Organisations typically encounter the operational impact only after a breach, a failed audit, or a malicious replay event, at which point data in transit becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-53 Rev 5, NIST Zero Trust (SP 800-207) and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.DS-2 | Addresses data-in-transit protection through safeguards for information as it is transmitted. |
| NIST SP 800-53 Rev 5 | SC-8 | Defines transmission confidentiality and integrity protections for data moving across networks. |
| NIST Zero Trust (SP 800-207) | Zero trust assumes no implicit trust for traffic, including data moving between systems. | |
| OWASP Non-Human Identity Top 10 | NHI guidance covers how workload and service identities protect exchanged data and secrets. | |
| NIST SP 800-63 | AAL2 | Digital identity assurance informs how strongly users or systems should authenticate before transfer. |
Apply transmission protection controls to preserve confidentiality and integrity in transit.
Related resources from NHI Mgmt Group
- Who is accountable when exact data matching fails to catch protected data in transit?
- How should security teams choose an AES mode for protecting sensitive data at rest and in transit?
- How should organisations protect long-lived sensitive data in transit as post-quantum risk becomes real?
- Why do TLS certificates matter for protecting login and payment data in transit?
