SecurityOverview

Start With the Workflow

An AI workflow may use several tools and data sources to complete a task. We begin by mapping those connections, the actions the system can take, and the decisions your team wants to keep.

We then design and test access limits, approvals, monitoring, and safe failure paths around that real workflow.

Access & Identity Management

Role-based access and least-privilege policies control who can configure, supervise, and use the system, and what each role can do.

Infrastructure & Runtime Security

We select encryption, network controls, logging, alerts, runtime monitoring, and hardened configurations to match the data and actions in your workflow. Your team can review the implemented controls before launch.

Workload Identity & Secrets

Each workload gets its own least-privilege identity, and sensitive credentials are kept in managed secret storage. Authorization rules define which data and tools each agent may access.

Identity and access controls are matched to user roles and system actions, and they connect to your enterprise identity and security systems.

Shared Responsibility, Written Down

Before implementation begins, we map security responsibilities across the cloud services, your organization, and the application. Your team can see who owns each area:

• Identity and data access
• Encryption and networking
• Monitoring and application behavior
• Incident response

Limits on What the System Can Do

Autonomy is treated as a managed capability, not a default. Each design sets explicit limits on autonomous actions and defines when the workflow stops and asks a person to decide.

• Risk assessments identify consequential actions that need human review or approval before they run
• Escalation can be triggered by confidence thresholds, financial limits, sensitive data, or unusual conditions
• Operators can pause the workflow, narrow tool access, require approval, or move to a fallback path
• Systems that can take consequential actions include suspension, rollback, or containment mechanisms suited to their scope

Monitoring & Incident Response

We log actions, decisions, model versions, and human interventions, and we define detection thresholds for drift, anomalies, and degraded performance.

• Fallback, shutdown, and escalation behavior is specified before launch
• Incident procedures cover drift, unsafe actions, degraded tools, and budget exceptions
• Records are kept for review and investigation, with defined access and retention

Operating reviews can then track detection, response, recovery, and escalation time against the thresholds your team agreed.

Change Control

Policy checks for IAM, infrastructure, agent tools, secrets, prompt changes, and pull requests run before changes reach production. Approved updates are tested and released with a rollback plan.

Start with a bounded workflow, and expand its permissions or reach only after testing and operating evidence support the change.

AWS Services We Commonly Use

For workloads on AWS, the governance layer is built around your existing identity, networking, encryption, and data controls. Depending on the design, that can include:

• AWS IAM for agent identity
• AWS CloudTrail for action logging
• Amazon CloudWatch for application telemetry
• Bedrock AgentCore for session isolation and credential management
• Bedrock Guardrails for content and safety policies

Before launch, we map each service and integration so your team knows where data is processed and who can access it.