Learn modern API security best practices for protecting sensitive data across cloud, SaaS, and hybrid environments – beyond gateways and auth – to secure APIs end-to-end.
API Security is the discipline of protecting the integrity, confidentiality, and availability of Application Programming Interfaces (APIs), which now carry 70%+ of global internet traffic.
While traditional security focused on securing the network "pipe", modern API security must focus on ensuring the "payload" ⸺ the actual data passing through those pipes.
Malicious actors have shifted tactics.
They no longer burn energy trying to break down the front door (network firewalls); instead, they exploit the API's logic to request data they shouldn't have.
A proactive API security strategy effectively masks data, validates logic, and fragments storage to make the enterprise resilient to breaches.
API vulnerabilities are weaknesses in the design, implementation, or operation of application programming interfaces. Attackers use them to gain unauthorized access, exfiltrate sensitive data, or disrupt services.
Recent industry reports show API data breaches increasing sharply year over year, with both the number of incidents and the number of records exposed rising dramatically.
In a BOLA scenario, the API accepts an object identifier from the client (e.g., a customer ID, order ID, or account ID). Still, it fails to verify whether the caller is authorized to access that object.
By tampering with IDs in paths or query parameters, attackers can read or modify other users’ records. OWASP identifies BOLA as API1:2023, the top risk for APIs today.
This is where weak login flows, long-lived tokens, and incorrect token validation allow attackers to impersonate legitimate users or services. This includes stolen API keys, poorly implemented JWT validation, and misconfigured OIDC/OAuth flows.
Once inside, attackers can call APIs exactly as a legitimate user or service would, making their activity difficult to distinguish from regular traffic.
With BOPLA, the API returns entire objects and relies on the client to hide sensitive fields.
When this behaviour is combined with missing field-level authorization checks, it exposes sensitive properties such as SSNs, card data, internal flags, or health details.
OWASP API3:2023 explicitly combines excessive data exposure and mass assignment into this category.
Security misconfiguration covers everything from verbose error messages and default credentials to unneeded HTTP methods and open debug endpoints. Each of these expands the attack surface.
Misconfigurations in gateways, WAFs, or service meshes can also bypass expected protections or expose administrative functionality that should never be reachable.
If APIs do not enforce rate limits, quotas, or size limits, attackers can flood them with expensive operations or oversized payloads. The result is often denial-of-service conditions or spiralling cloud costs.
NIST SP 800-228 explicitly ties DoS and “unrestricted resource consumption” to controls at the API layer, not just to network edge devices.
Many organizations run APIs that no one has fully documented: deprecated versions, testing endpoints, or internal services that quietly moved into production.
These “shadow” or “zombie” APIs often lack up-to-date security measures, making them easy entry points. Studies consistently show that only a minority of organizations maintain a complete, up-to-date API inventory.
Traditional API security tools focus on configuration, code scanning, and gateway rules. A payload-centric approach adds a second line of defence: even when one of these vulnerabilities is exploited, the attacker still cannot turn exposed data into business-impacting loss.
Enterprises securing APIs at scale face several recurring challenges that configuration and code scanning alone rarely solve.
Microservices, SaaS integrations, and AI services all increase the number of APIs and versions in play. Reports from multiple vendors now show that APIs dominate application traffic and attack volume, with a majority of application-layer attacks targeting APIs specifically.
Each cloud provider offers its own gateways, WAFs, identity services, and logging models. On-premises systems add yet another set of controls and patterns.
Achieving consistent API security controls across data centers, multiple clouds, and SaaS providers is difficult, especially when legacy applications cannot be easily refactored.
Many critical APIs sit in front of mainframes, older ERP platforms, or custom applications that predate modern security patterns. These systems are difficult to modify, yet they handle some of the most sensitive data in the environment.
Security teams must protect these interfaces without assuming they can be rewritten in the short term.
Modern teams ship features continuously. Security reviews and manual approvals struggle to keep pace, leading to drift between the intended API security posture and the actual deployment.
NIST SP 800-228 underscores the need to weave controls into DevSecOps pipelines and to rely on automation wherever possible, rather than relying solely on periodic manual checks.
Even when APIs are known and documented, security teams often lack a clear view of which endpoints handle regulated data, which ones are externally exposed, and which connect to third parties.
Without this context, prioritizing remediation and applying strong controls where they matter most becomes guesswork rather than a data-driven process.
APIs link web apps, mobile clients, back-end systems, and SaaS integrations. They form the backbone of digital environments. But because they handle sensitive data with predictable patterns, APIs have become a preferred vector for attackers.
Reports across the industry – including OWASP’s API Security Top 10, and recent breach analyses – show that:
Securing APIs today requires understanding how data flows, who can see it, and how to reduce the impact of inevitable API failures.
These fundamentals remain essential and form the baseline for compliance and operational security.
APIs typically rely on OAuth 2.0, OpenID Connect, or JWTs to authenticate callers.
Tokens help establish identity, but they only work when combined with object-level and property-level authorization.
BOLA remains the most exploited API flaw, allowing users to query or modify data they are not permitted to access. Implement role-appropriate access at both the object and field levels.
Apply TLS 1.2+ or, preferably, TLS 1.3 to all API traffic. Transport encryption protects credentials and payloads from interception, but only while in transit. Combine it with strong key rotation and encrypted storage to guard data after it reaches a system.
Validate inputs on the server (ideally at the gateway) using schemas. Enforce strict type requirements and reject unexpected parameters to block injection attacks and mass-assignment vulnerabilities.
Rate limiting and throttling help slow brute-force attacks and mitigate denial-of-service attempts. Distributed limits across microservices close gaps created by horizontal scaling.
Organizations frequently operate undocumented APIs – i.e., test endpoints, deprecated routes, forgotten internal services. Continuous discovery establishes visibility, enabling security teams to enforce consistent controls.
Most API compromises share a familiar pattern:
The failure isn’t at the perimeter; instead, it's at the payload.
A modern API security strategy must assume attackers will gain access; instead, focus on ensuring data remains protected even in the face of unauthorized use.
These best practices reflect a modern API security strategy: one that secures the data itself rather than relying solely on perimeter devices, cloud configurations, or code-level checks.
Each recommendation focuses on technologies and architectural patterns that materially reduce risk, even when attackers successfully authenticate or exploit API logic flaws.
Discover APIs by inspecting live network traffic, not just cloud resource configurations or code repositories.
How it works:
Network-layer discovery uses DNS monitoring, packet inspection (header-level), and traffic flow analysis to identify every active endpoint:
Why it’s better: cloud-native scanners only see what is declared in the cloud infrastructure. Network-level discovery reveals what is actually communicating, which is the source of nearly all real API risk.
Use TLS 1.3 with Perfect Forward Secrecy, then apply tokenization before transmission so sensitive values never traverse internal networks in plaintext.
TLS 1.3 removes legacy ciphers and protects sessions, while tokenization substitutes sensitive data (PII; PCI; health data) with format-preserving tokens within the traffic stream.
Why it’s better: Transport encryption ends once the connection terminates. Tokenization prevents exposure even if internal logs, caches, message queues, or downstream services are compromised.
Sign JSON Web Tokens (JWTs) using asymmetric algorithms such as RS256 rather than symmetric algorithms like HS256.
How it works:
Why it’s better: a breach of any symmetric key holder allows an attacker to forge tokens. Asymmetric signing eliminates this single point of failure and dramatically reduces blast radius.
Store and transmit sensitive values as tokens or references rather than raw data so that object identifiers cannot reveal actual records.
How it works:
Instead of storing sensitive attributes directly, systems store:
The real data lives in a secure, isolated vault.
Why it’s better: even if a BOLA flaw exists, the attacker only retrieves tokens or references, not the underlying data. BOLA becomes low-impact instead of catastrophic.
Apply schema enforcement at the network or gateway layer to block malformed or unexpected API requests before they reach backend services.
How it works:
Why it’s better: Application-level validations are prone to inconsistencies across services. Centralized inline validation ensures uniform enforcement and eliminates entire classes of mass-assignment and injection vulnerabilities.
Transform response payloads in transit based on user roles, context, and policies to prevent unnecessary fields from leaving the system.
How it works:
A policy engine inspects outbound payloads and:
Why it’s better: APIs often return full objects for convenience. Dynamic masking ensures excessive data exposure never reaches clients ⸺ even if the application itself returns more data than intended.
Break sensitive data into multiple encrypted fragments stored in different physical or cloud locations.
How it works:
Why it’s better: traditional encryption still creates high-value targets for attackers. Fragmentation ensures that compromising one cloud account, VM, or database yields no usable data.
Apply rate limits using a global, distributed state store so that all API instances share the same throttling view.
How it works:
Why it’s better: Local rate limits operate only within a single instance, so attackers can easily bypass them by shifting traffic across nodes, regions, or IP addresses. Distributed rate limiting centralizes the decision-making, ensuring every API instance reads from the same global state.
This closes evasion gaps created by horizontal scaling, prevents attackers from resetting their allowance by routing to a different node, and provides accurate throttling for multi-cloud or multi-region architectures.
Never embed API keys, credentials, or tokens in source code. Inject secrets dynamically at runtime.
How it works:
Why it’s better: hard-coded secrets are easily extracted from repos, containers, CI logs, or client applications. Runtime injection removes them from the attack surface entirely.
Replace production data with synthetic datasets that mimic structure and relationships, without exposing actual values.
How it works:
synthetic data generators create datasets with:
Applications function normally, but no actual PII leaves production.
Why it’s better: most internal data leaks originate from Dev/Test environments. Synthetic data allows full functionality without transferring sensitive data outside controlled systems.
Use an API gateway or service mesh as the central enforcement point for critical security controls.
How it works:
Gateways consistently enforce:
Why it’s better: embedding security in every microservice creates drift, and inconsistent coverage. Centralizing controls ensures uniform protection, and reduces the cognitive load on development teams.
Modern API security breaks down when organizations rely solely on gateway rules, static schemas, or developer-driven refactoring.
These methods cannot keep pace with growing API estates, hybrid infrastructure, legacy systems, and the rapid surge of sensitive data flowing through distributed environments.
DataStealth closes these gaps through a unified Data Security Platform (DSP) that discovers, classifies, and protects sensitive data wherever it moves ⸺ including every API, microservice, and integration point ⸺ without requiring agents, SDKs, code changes, or invasive deployments. It is designed for the scale, speed, and complexity of today’s enterprise.
Where most API security technologies require agents, sidecars, rewrites, or instrumentation, DataStealth deploys with a single DNS change.
This enables:
This architecture removes the operational burden typically associated with API security rollouts.
Enterprises can’t protect what they can’t see.
DataStealth’s discovery engine scans across, on-prem, cloud, SaaS, and legacy environments to identify every active endpoint ⸺ e.g. REST, gRPC, GraphQL, file-based APIs, and more ⸺ without predefined search locations or integrations.
Distributed scanning, and satellite nodes ensure coverage across regions, and compliance zones, delivering a complete, real-time API inventory tied to lineage, sensitivity, and policy context.
API gateways control access, but they cannot protect the data itself.
DataStealth applies:
All applied in real-time, at the network layer, without altering application code or database schemas. This ensures that even when APIs return excessive data or if misconfigurations occur, the payload is already devalued.
Every component of DataStealth is engineered for enterprise-grade resilience:
Organizations can expand protection across hundreds of microservices or thousands of APIs without architectural rework.
DataStealth integrates seamlessly into any environment:
Its architecture preserves formats so existing apps, schemas, and workflows continue working without modification, ensuring rapid adoption across diverse systems.
Because DataStealth protects the data itself ⸺ not just the perimeter ⸺ it helps organizations meet:
Policy-as-code, audit logs, separation of duties, and role-based visibility ensure enforcement is provable, consistent, and reviewable.
The platform’s combined architecture ⸺ network-layer discovery, inline protection, data fragmentation, and zero-trust enforcement ⸺ ensures:
The data itself remains inaccessible and useless to unauthorized parties. Ready to protect every API in your estate without rebuilding your systems? Book a demo.
Bilal is the Content Strategist at DataStealth. He's a recognized defence and security analyst who's researching the growing importance of cybersecurity and data protection in enterprise-sized organizations.
