SAILLENTBook a DemoSAILLENT provides runtime enforcement for autonomous AI agents. It evaluates covered actions against explicit security policies and allows or denies requests before they are forwarded through the protected execution path.
From agent identity and tool permissions to policy decisions and audit records, SAILLENT is designed to help organizations establish control over what their AI systems are permitted to do.
Intercept. Inspect. Enforce.
Policy decisions before covered actions are forwarded.
Explicit rules governing permitted agent and tool actions.
Unmatched requests are denied under the runtime's default-deny policy behavior.
Versioned YAML policies with GitOps-oriented workflows and runtime policy updates.
Recorded enforcement decisions with available agent, tool, reason, and policy context.
Runtime deployment options designed to support customer-controlled environments.
Normalization and inspection capabilities designed to help identify risky or evasive inputs.
Technical architecture, deployment, data-flow, and control reviews with prospective customers.
Note: Specific controls, integrations, deployment options, and evidence availability depend on the deployed version and configuration.
Security where AI decisions become actions. Model safeguards can influence what an AI system proposes. Identity and access management can establish which identities have permission to access a resource. SAILLENT addresses a different part of the security problem: evaluating covered actions against runtime policy before they proceed through a protected execution path.
SAILLENT evaluates covered requests outside the model's own decision-making process. The runtime makes an allow-or-deny decision according to applicable policy rather than relying solely on the model to follow instructions.
Security rules define which agent and tool combinations are permitted to perform covered actions. When no applicable policy authorizes a request, the runtime's default-deny behavior rejects it.
Policies are represented in structured YAML and can be managed through GitOps-oriented workflows. The runtime supports policy updates and last-known-good behavior when a policy source becomes unavailable.
The runtime includes normalization steps intended to reduce common input-evasion opportunities, including URL encoding, selected encoded or compressed content, Unicode normalization, zero-width characters, and selected homoglyph substitutions. Normalization is a defensive layer, not a guarantee that every adversarial input or prompt injection will be detected.
Enforcement decisions can be recorded with available context, including the agent, tool, decision, reason, and policy information. These records help operators investigate what the runtime evaluated and how it responded.
SAILLENT's runtime architecture supports self-hosted deployment patterns. Deployment and data-flow requirements should be reviewed against the customer's chosen model providers, connected systems, and control-plane configuration.
The agent's authority should have boundaries. An AI agent may be able to access a tool without being authorized to perform every action that tool exposes. SAILLENT is designed to evaluate covered tool requests against explicit policy.
Security engineering, SOC, compliance, and the agents themselves each interact with the enforcement plane at exactly the level they need — nothing more.
Policies can constrain which actions are permitted for a given agent and tool combination. Allow explicitly authorized operations. Deny prohibited operations. Apply default-deny behavior when no applicable policy authorizes the request. Maintain policy definitions that can be reviewed and managed as code.
A model's decision to invoke a tool is not itself authorization to execute the requested operation. SAILLENT evaluates covered requests at the enforcement boundary. The result depends on the request context, the applicable policy, and the runtime's supported integration path.
The objective is to make the policy decision before the covered request is forwarded to its downstream target. A deployment must be configured so protected traffic actually traverses the enforcement point. Requests that bypass that path are outside its enforcement coverage.
SAILLENT complements existing security controls rather than replacing them. Identity providers, IAM permissions, network segmentation, secrets management, endpoint protection, infrastructure controls, and human approvals remain important.
The policy decision happens before the action proceeds. Autonomous agents can be manipulated by untrusted instructions, unsafe tool descriptions, compromised context, or unexpected model behavior. A security architecture should not depend on every model decision being correct. SAILLENT introduces a policy enforcement point between covered AI requests and protected downstream capabilities.
A covered request reaches the configured SAILLENT enforcement boundary.
The runtime processes the request and evaluates it against the applicable policy and available request context.
The runtime allows or denies the request according to the policy decision.
The enforcement decision is recorded with the context available to the runtime, supporting subsequent investigation and review.
Agent: coding-agent-prod Requested tool: deployment.promote_release Target: production Policy: coding-agent-production-boundary Decision: DENY Reason: Production promotion is not authorized for this agent. Downstream request: Not forwarded through the protected path
Illustrative event for explanatory purposes. This is not a claim about a real customer incident.
The objective is straightforward: an agent's request should not become a protected system action unless the enforcement policy permits it.
The controls around AI must be enforceable. AI governance requires more than written rules or a record of what happened after the fact. Security teams need policies that can be applied to covered actions and decisions that can be examined.
Define permitted actions rather than assuming that access to a tool implies permission to use every capability it exposes.
Evaluate covered requests at the enforcement point before forwarding them to protected downstream systems.
Manage policy definitions through structured, version-controlled workflows and review changes to the rules governing agent behavior.
Test representative attack scenarios, prohibited actions, policy mismatches, and relevant input-evasion techniques against the actual deployment.
Review recorded enforcement decisions and available context to understand why a request was allowed or denied.
Integrate runtime enforcement with existing identity, infrastructure, monitoring, and organizational security controls.
The principle: govern the actions an AI system can take, not just the responses it produces.
Security policy should be reviewable, not opaque. Runtime enforcement is only as reliable as the policies and deployment paths that govern it.
SAILLENT uses structured policy definitions to express applicable agent and tool constraints. This makes policy intent easier to inspect and manage than relying on free-form model instructions alone.
The runtime supports periodic policy synchronization and hot-swapping of policy state. Its last-known-good behavior is designed to preserve the last successfully loaded policy state when the policy source is temporarily unavailable. The behavior of an individual failure scenario should be validated against the deployed version and configuration.
Requests without an applicable authorizing policy are denied by default. Teams should still test policy matching, policy conflicts, error handling, and integration coverage as part of deployment acceptance.
Decision records support investigation of enforcement outcomes. Customers should define their own retention, access, backup, monitoring, and incident-response requirements for the environment in which the runtime and its audit data operate.
Built for serious technical evaluation. Security teams should be able to understand where SAILLENT sits, what it evaluates, which requests it covers, and how it behaves when something goes wrong. We encourage prospective customers to evaluate the runtime against their own architecture, threat model, and acceptance criteria.
Review the runtime's position in the execution path, the supported integration model, external dependencies, and the movement of requests between agents, model providers, and downstream tools.
Establish which agent actions and integration paths are protected, which threats are in scope, and which requests or systems remain outside the enforcement boundary.
Review policy definitions, authorization decisions, default-deny behavior, update workflows, and relevant failure modes.
Understand which decision fields are recorded, where records are stored, who can access them, and what retention and export mechanisms are available in the deployment being evaluated.
Review deployment architecture, connectivity requirements, licensing dependencies, external services, and data flows against your security and residency requirements.
Agree on representative and adversarial scenarios, expected policy decisions, failure handling, and measurable acceptance criteria before production rollout.
SAILLENT is designed to enforce policy at a configured runtime boundary. It evaluates covered requests and allows or denies them before forwarding. The exact enforcement coverage depends on the integration and deployment path.
No. SAILLENT complements IAM and other security controls by evaluating covered actions against runtime policy. Identity, infrastructure permissions, and network controls remain important defense-in-depth measures.
When a request does not match an applicable policy that authorizes the requested action, the runtime's default-deny behavior rejects it. Customers should validate policy coverage and matching behavior against their specific integrations and use cases.
The runtime supports policy synchronization and hot-swapping of policy state. Updates are subject to the configured policy source, synchronization interval, and deployed version.
The runtime supports last-known-good policy behavior when the policy source is unreachable. Customers should validate startup, stale-policy, recovery, and other failure scenarios against their required operational and security objectives.
The runtime's audit records can include enforcement decision context such as the agent, tool, decision, reason, and policy information. The exact fields and storage behavior depend on the deployed version and configuration.
SAILLENT provides runtime enforcement and input-inspection capabilities intended to help manage risks associated with adversarial inputs and unauthorized agent actions. No security control should be represented as detecting or preventing every prompt injection. The most important evaluation is whether prohibited actions are denied on the protected execution path.
SAILLENT is designed to support covered MCP interactions. Specific server configurations, tool calls, and integration paths should be validated during technical discovery and acceptance testing.
SAILLENT supports self-hosted runtime deployment patterns. Specific customer VPC, on-premises, air-gapped, and other deployment requirements must be confirmed against the version and commercial plan being evaluated.
That depends on the complete deployment and integration architecture, including the selected model provider and any external services. During security review, we map which requests and data are processed by the runtime, which are sent to model providers, and what information is exchanged with management or licensing services. We recommend documenting these flows explicitly for each production deployment.
Please request the current security and assurance documentation for the product. Any certification, independent assessment, or audit status should be stated only when supported by current, verifiable evidence.
A Proof of Value can be scoped around selected workflows, policy requirements, adversarial scenarios, and measurable acceptance criteria. The objective is to verify enforcement behavior in your environment rather than rely solely on a product demonstration.
Autonomous AI introduces a new operational question: not just what an agent can generate, but what it can actually do.
SAILLENT gives security teams a runtime policy enforcement layer for covered AI actions—so authorization can be evaluated before those actions proceed.
Bring your architecture, threat model, and hardest test cases. Let's establish what needs to be protected and validate the enforcement boundary together.
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Capability claims only — formal certifications & attestations in progress · Technical evidence available under NDA
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