AI Model Settings (Pro)

Note: Sensei is a DefectDojo Pro-only feature and is currently in BETA.

When Sensei runs a scan or applies a fix DefectDojo-hosted (server-side, rather than in your own CI), it needs an LLM. AI Model Settings is where you choose that model and supply its credentials, once, instance-wide — the hosted worker uses this configuration instead of any per-repository secret.

You reach it from the sidebar under Sensei + AI → AI Model Settings. You need a global Maintainer or Owner role to change it.

On-premise only. This page exists only on on-premise (“local”) deployments, where you bring your own model. On DefectDojo Cloud the LLM is managed for you and this page is not shown.

Providers#

Pick a provider from the LLM Provider dropdown. The fields below the dropdown change to match the provider you choose.

Every provider also accepts an optional Model override (leave blank to use the provider’s default) and an optional API Base URL to point at an on-prem or self-hosted gateway.

All secrets are encrypted at rest and are write-only: once saved, the form shows only whether a secret is set, never its value. Leave a secret field blank when saving to keep the stored value; type a new value to replace it.

Claude (Anthropic) and OpenAI#

These providers use a single API key:

1. Model (optional) — e.g. a specific Claude or OpenAI model. Blank uses the provider default.
2. API Base URL (optional) — point at a self-hosted gateway instead of the provider’s public API. Blank uses the default (https://api.anthropic.com / https://api.openai.com).
3. LLM API Key — the provider API key.

Amazon Bedrock#

Amazon Bedrock hosts Anthropic Claude models in your own AWS account, and it authenticates with AWS credentials rather than a single key, so its fields differ:

1. Model (optional) — a Bedrock model id or inference-profile ARN, e.g. anthropic.claude-3-5-sonnet-20241022-v2:0, or a cross-region inference profile like us.anthropic.claude-sonnet-4-5-.... Some newer models are only reachable through an inference profile. Blank uses the engine default.
2. API Base URL (optional) — a custom Bedrock endpoint (for a VPC endpoint or FIPS). Blank uses the default AWS endpoint for the region.
3. AWS Region — required. The region hosting the model, e.g. us-east-1.
4. AWS Access Key ID / Secret Access Key / Session Token — optional (see below).

How Bedrock access is determined#

The region is always required, but the AWS credentials are optional because the Sensei engine resolves them through the standard AWS credential chain, in this order:

1. The static keys you enter here, if any. Provide an Access Key ID and Secret Access Key (and a Session Token for temporary STS credentials) to authenticate as a specific IAM identity.
2. Otherwise, the engine’s ambient AWS identity. With the key fields left blank, the engine uses whatever IAM role is attached to where it runs — an IRSA role on EKS, an EC2 instance profile, an ECS task role, or a Lambda execution role.

Leaving the keys blank is the recommended setup when the engine already runs inside AWS: the workload’s own IAM role grants Bedrock access, so there are no long-lived keys to store or rotate. Either way, the identity used needs permission to invoke the Bedrock model (bedrock:InvokeModel) in the chosen region.

Test connection#

Test connection validates the configuration before a scan relies on it:

• For Claude / OpenAI, it makes a minimal authenticated call to the provider.
• For Amazon Bedrock with static keys, it lists the region’s foundation models to confirm the credentials work.
• For Amazon Bedrock without static keys, it reports that the engine’s instance IAM role is used at runtime — DefectDojo can’t validate that role on the engine’s behalf, so confirm Bedrock access from the engine’s own environment.

Save#

Save Settings stores the configuration for the whole instance. From then on, DefectDojo-hosted Sensei scans and fixes use this model. See Fixing Findings with Sensei for how fixes run once a model is configured.