AI agents, applications, and scripts all need a way to authenticate when they access protected systems. Secrets management tools have long helped solve that problem by storing, distributing, and rotating credentials so a database password or API key doesn’t end up hardcoded into a config file or committed to a repository.
That is a meaningful improvement over unmanaged secrets, and modern platforms have gone further with dynamic, short-lived credentials. But the credential remains the center of the security model: how it is created, where it is stored, who can retrieve it, and when it expires.
Aembit takes a different approach. Rather than making the credential the control point, it governs the access request itself, using the identity of the agent or workload, policy, and runtime context to determine whether access should be granted. When a credential is required downstream, Aembit can obtain or issue it at the time of access rather than requiring the agent or workload to carry a standing credential
What to Evaluate in a Secrets Management Tool
The eight tools below differ less on individual features than on a few structural questions that determine where each one fits.
- Deployment model. Self-hosted platforms, like Vault’s Community Edition or Conjur’s open source tier, give you full control over where secrets physically live, at the cost of operating that infrastructure yourself. Managed SaaS platforms remove that overhead in exchange for trusting another vendor with your credential infrastructure.
- Static storage vs. dynamic secrets. A basic secrets store hands out the same credential until something rotates it. A dynamic secrets engine generates a new, short-lived credential for each request, which shrinks the window a leaked credential stays useful.
- Identity model and multi-cloud reach. The three native cloud vaults are each built around their own cloud’s IAM. That may be sufficient for a single-cloud environment, but it becomes a constraint when access spans multiple clouds or hybrid infrastructure.
- What’s requesting access. AI agents and automated workloads increasingly require access decisions based on their identity, runtime context, and the resource being requested. Static application configuration may still require a conventional secrets store, while privileged human access remains the domain of PAM.
Certification status and audit logging depth also vary by vendor and change over time. Check every vendor’s current trust or compliance page directly rather than relying on a point-in-time claim.
Ranking the Top Secrets Management Platforms
| Tool | Best For | Deployment | Pros | Cons |
| Aembit | AI agents and workloads accessing APIs, MCP servers, SaaS, and data | Managed SaaS; brokers access and short-lived credentials for agents and workloads | Keeps long-lived credentials out of agent and workload runtimes; can evaluate agent and user identity together through blended identity | Not a general-purpose secrets store for static credentials; smaller install base than established platforms |
| HashiCorp Vault | Multi-cloud enterprises needing a full-featured, self-managed engine | Self-hosted (Community/Enterprise) or managed via HCP Vault Dedicated | Broad support for dynamic secrets and integrations; large ecosystem and Terraform support | Significant operational overhead; lighter SaaS tier (HCP Vault Secrets) discontinued in 2026 |
| AWS Secrets Manager | Teams running primarily on AWS | Managed SaaS, AWS-native only | Deep integration with AWS IAM, RDS, and Lambda; no infrastructure to run | Built-in rotation covers only RDS, Redshift, and DocumentDB; tied to AWS IAM |
| Azure Key Vault | Organizations standardized on Azure and Entra ID | Managed SaaS, Azure-native only | Covers secrets, keys, and certificates in one service, tied to Entra ID | Rotation support varies by secret type; identity model doesn’t extend to multi-cloud |
| Google Cloud Secret Manager | Large secret volumes on Google Cloud | Managed SaaS, GCP-native only | Simple per-version secret model; integrates with Google Cloud IAM and Cloud Build | Fewer dynamic secret engines than Vault; same single-cloud limitation as AWS and Azure |
| CyberArk Conjur | Enterprises already running CyberArk for PAM | Self-hosted (open source) or commercial enterprise deployment | Mature policy-as-code approach with deep CI/CD integration; benefits from CyberArk’s PAM tooling and audit maturity | Deployment complexity closer to a PAM platform than a secrets store; 2026 Palo Alto Networks acquisition adds roadmap uncertainty |
| Doppler | Developer teams wanting fast, managed configuration secrets | Managed SaaS only, no self-hosted option | Strong developer experience with environment-based secret management and a simple CLI-based local workflow | Historically strongest in configuration secrets; dynamic secrets support is narrower than Vault’s broader engine ecosystem |
| Infisical | Startups and open-source-first teams | Self-hosted (open source) or managed cloud | Open source core with native Docker, Kubernetes, and GitHub Actions integrations; active development pace | Several advanced capabilities, including dynamic secrets and longer audit retention, require higher-priced tiers |
1) Aembit
Aembit is an identity and access management platform for AI agents and workloads, not a secrets store. It verifies the identity of an agent, application, or service, evaluates access policy and runtime context, and obtains or issues short-lived credentials based on the policy and context of the request. For agents acting on behalf of a user, Aembit’s blended identity model evaluates the agent’s identity and the user’s identity together in the same access decision. It also provides OAuth 2.1 authorization for MCP clients, a use case most of the tools on this list don’t natively cover, and applies policy and credential controls to communication between agents and the MCP servers they call. A $300B investment firm used Aembit to eliminate long-lived credentials stored in Claude and MCP server configurations, replacing them with short-lived, policy-scoped tokens issued per session.
2) HashiCorp Vault
HashiCorp Vault, owned by IBM, offers one of the broadest dynamic secrets capabilities most teams will evaluate, generating on-demand, short-lived credentials for databases, cloud IAM, and PKI certificates rather than just storing static ones. IBM completed its acquisition of HashiCorp in February 2025, and Vault now carries IBM’s enterprise support behind it. Teams that picked HCP Vault Secrets, HashiCorp’s lighter managed tier, need a migration plan; it was discontinued in 2026, with customers directed to HCP Vault Dedicated or Community Edition.
3) AWS Secrets Manager
AWS Secrets Manager is the default choice for teams already committed to AWS, with no separate infrastructure to run. Built-in rotation covers specific native integrations, like RDS, Redshift, and DocumentDB; other secret types need a custom Lambda function.
4) Azure Key Vault
Azure Key Vault covers secrets, encryption keys, and certificates in a single managed service, tied natively to Microsoft Entra ID. Built-in rotation coverage varies by secret type, and full automation often requires a custom Azure Function rather than a native toggle.
5) Google Cloud Secret Manager
Google Cloud Secret Manager integrates directly with Google Cloud IAM and Cloud Functions for rotation, though most automated rotation setups still require custom function code rather than a built-in rotation engine.
6) CyberArk Conjur
CyberArk Conjur brings a policy-as-code approach to machine secrets, with deep CI/CD pipeline integration and the audit maturity of CyberArk’s broader PAM platform. It fits naturally for enterprises that already run CyberArk for privileged access management and want machine secrets on the same policy model. Broader platform scope can bring more deployment complexity than teams need for traditional secrets management.
7) Doppler
Doppler is often cited as the most polished developer experience among secrets management tools, with environment-based secret organization, versioning, and simple local development sync through its CLI. Its roots are in application configuration and developer workflows, while its dynamic secrets capabilities are narrower than Vault’s broader set of credential engines.
8) Infisical
Infisical pairs an open source core with native integrations for Docker, Kubernetes, and GitHub Actions, letting small teams adopt real secrets management instead of relying on .env files. Several advanced capabilities, including dynamic secrets, automated rotation, and longer audit retention, require paid or higher-tier plans. Its enterprise track record is shorter than CyberArk’s, Vault’s, or the major cloud vaults’, which matters for regulated industries with vendor due-diligence requirements.
Choosing the Right Secrets Management Approach
These tools solve overlapping but distinct problems, and they are not necessarily substitutes for one another. The more useful question is what kind of access you need to protect and whether that access still requires a stored credential.
Aembit addresses a different layer: runtime access for AI agents and workloads. It verifies identity and evaluates policy and context at the time access is requested, then obtains or issues the short-lived credential needed downstream.
A cloud-native secrets manager often still needs to hold static secrets that legitimately require storage, like a legacy application’s database password. Aembit can run alongside that secrets store, using identity-based access for AI agents and workloads while the vault continues to manage credentials that still need to exist.
If AI agents and workloads are the access problem you’re trying to solve, talk to an Aembit engineer about replacing the standing secrets in that part of your stack, or get a free Aembit tenant and see the policy engine issue a scoped credential in real time.
