Driver AI vs Sourcegraph Cody for security: SSO/SCIM, RBAC, auditability, and private deployment options

Security teams evaluating AI coding assistants increasingly care less about clever features and more about hard guarantees: SSO/SCIM integration, granular RBAC, auditability, and private deployment options that keep code and secrets under control. When you compare Driver AI vs Sourcegraph Cody for security, those are exactly the areas where you should focus your checklist.

This guide walks through how each product stacks up across identity and access management, regulatory-grade logging, deployment models, data residency, and how they fit into a broader security-conscious SDLC.

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Why security posture matters for AI coding assistants

AI coding assistants sit directly in developers’ IDEs and chat workflows, often touching:

• Proprietary source code
• Secrets and configuration
• Infrastructure details and deployment scripts
• References to customer data and internal APIs

That makes them high‑value targets and high‑risk integrations. A secure solution must:

• Plug into existing identity (SSO/SCIM) and authorization (RBAC) systems
• Provide full auditability for enterprise compliance
• Offer private deployment options to keep sensitive code and prompts in‑house
• Minimize external data sharing and long‑term retention

With that lens, here’s how Driver AI and Sourcegraph Cody compare.

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Identity and access management: SSO and SCIM

Sourcegraph Cody

Sourcegraph has a long history in enterprise environments, and Cody inherits that maturity.

SSO support

Typical capabilities you can expect from Sourcegraph/Cody in enterprise mode include:

• SAML SSO with major IdPs:
  • Okta
  • Azure AD / Entra ID
  • Google Workspace
  • OneLogin and other SAML‑compatible providers
• OIDC support where OIDC is preferred over SAML
• Just‑in‑time user provisioning via SSO logins

SCIM provisioning

For large teams, SCIM is essential to keep access aligned with HR/IT systems. In enterprise deployments, Sourcegraph commonly supports:

• SCIM user provisioning
  • Automatic user creation based on IdP groups
  • Deprovisioning when employees leave or change roles
• Group sync
  • Mapping IdP groups to Sourcegraph/Cody roles, organizations, or teams

The net effect: if your enterprise already uses Okta, Azure AD, or similar, Sourcegraph Cody can typically become just another managed enterprise app with lifecycle automation.

Driver AI

Driver AI is newer compared to Sourcegraph’s long‑standing enterprise platform. Its approach to SSO/SCIM can vary by edition and deployment model, but key considerations include:

SSO

• SAML/OIDC integration is usually available for enterprise plans
• Compatibility with standard IdPs via generic SAML/OIDC configuration
• Just‑in‑time provisioning often supported for new users logging in via SSO

SCIM

• SCIM support may be:
  • Available only on higher‑tier or “enterprise” Driver AI plans
  • Limited to certain IdPs or in more basic form (user create/deactivate, fewer group mappings)
• In some setups you may still rely primarily on SSO group claims instead of full SCIM for RBAC

IAM summary: Driver AI vs Sourcegraph Cody

• If you have complex identity lifecycles and strict HR→IdP→app provisioning policies, Sourcegraph Cody typically offers more mature, battle‑tested SSO/SCIM integrations.
• Driver AI can likely integrate with your IdP for SSO, but SCIM capabilities and depth of integration may be more limited or newer, depending on your plan and region.

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RBAC: Role‑based access control and least privilege

Sourcegraph Cody

Because Cody sits on top of the Sourcegraph platform, it benefits from an established RBAC and repository permission model.

Typical RBAC capabilities include:

• Org‑level and instance‑level roles
  • Admin, user, viewer, etc.
  • Custom roles or granular permissions in some editions
• Repository‑level permissions
  • Cody can only access code that a user could normally view in Sourcegraph
  • Aligns with existing Git host permissions (GitHub, GitLab, Bitbucket, etc.)
• Feature‑level controls (depending on version/edition)
  • Enable/disable Cody for particular organizations, teams, or users
  • Restrict specific capabilities (e.g., chat, code generation, search) per role or group
• Context control
  • Limit the scope of repositories or namespaces Cody can draw context from
  • Optionally exclude particularly sensitive repos from AI features

This lets security teams enforce least‑privilege: Cody cannot “see” more than the user’s normal access allows, and certain AI capabilities can be reserved for specific groups.

Driver AI

Driver AI’s RBAC setup typically focuses on controlling access to:

• Workspaces or projects
• Connected code repositories
• Administrative features (billing, configuration, model settings)

Expected RBAC patterns for Driver AI include:

• Admin vs member roles, with possible intermediate roles (e.g., workspace admin vs global admin)
• Repository access control
  • Users only see AI assistance for repos they are permitted to access
• Feature‑level toggles for particular groups (e.g., restrict code generation in high‑risk teams, or disable certain integrations)

Because the platform is younger than Sourcegraph’s, some of the more advanced or deeply integrated RBAC features may still be evolving. However, for many mid‑sized organizations, Driver AI’s workspace‑oriented RBAC may be sufficient.

RBAC summary

• Sourcegraph Cody: Strong alignment with existing repo permissions, mature RBAC within the Sourcegraph platform, and more granular control over Cody availability by org/team/repo.
• Driver AI: Clear separation of admin vs user, with repository scoping, but may be less granular depending on configuration and version.

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Auditability and compliance logging

For security, answering “who accessed what, when, and how?” is non‑negotiable. Audit logs are your foundation for incident response, insider threat detection, and compliance (SOC 2, ISO 27001, etc.).

Sourcegraph Cody

Because Cody is integrated into Sourcegraph, it benefits from platform‑level audit logging that can include:

• Authentication events
  • SSO logins and logouts
  • Failed login attempts
• Authorization and configuration changes
  • Role changes, group assignments
  • Repository access modifications
  • Admin actions affecting Cody settings
• Product usage events (depending on version)
  • Invocation of Cody chat or autocomplete
  • Repositories accessed as AI context
  • High‑level metadata about prompts or activities

Enterprises can typically:

• Export logs to SIEM systems like Splunk, Datadog, or Elastic
• Retain logs long‑term for compliance
• Use logs for monitoring anomalous usage patterns (e.g., mass queries across many sensitive repos)

Driver AI

Driver AI’s audit logging capabilities generally focus on:

• User activity
  • Logins and sessions
  • Prompt/response usage at a high level (what feature, which project, when)
• Admin actions
  • Changes in workspace or project configuration
  • Model or deployment settings updates
• Security events
  • Access violations or failed attempts
  • Integration changes (e.g., connecting/disconnecting a repo host)

Some key considerations for Driver AI enterprise deployments:

• Whether logs can be exported to your SIEM
• Level of granularity (user‑level vs team‑level vs aggregate)
• Whether prompts and responses are logged in full, redacted, or summarized
• Retention controls to align with your data governance policy

Auditability summary

• Sourcegraph Cody tends to offer deeper, more mature logging integrated with its core code‑search platform and existing enterprise customers’ compliance pipelines.
• Driver AI provides the core audit trail you’d expect from an enterprise SaaS, but you should validate how deep the logs go (especially around prompt content, code snippets, and repo access) and what integration options you have with your SIEM.

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Private deployment options and data residency

This is often the deciding factor for highly regulated industries (finance, healthcare, government) or companies with strict IP controls: can you run Driver AI or Sourcegraph Cody in a private environment, and how are models and data handled?

Sourcegraph Cody deployment models

Sourcegraph has historically offered multiple deployment modes:

1. Self‑hosted / on‑prem Sourcegraph

   • Deployed into your own:
     • Kubernetes clusters
     • Private cloud (AWS, GCP, Azure)
     • On‑premise data centers
   • Cody runs within that Sourcegraph environment
   • Code and metadata never leave your network (subject to model configuration)

2. Private cloud / VPC deployment

   • Sourcegraph hosted in a dedicated VPC or private cloud environment
   • Network isolation and peering options
   • Data residency controls based on region

3. Cody model configuration

   • You can often choose between:
     • Managed LLMs (e.g., hosted by Sourcegraph or their providers)
     • Self‑hosted / private models (e.g., local LLMs or your own OpenAI/Azure/AWS endpoints)
   • Ability to disable cross‑customer training on your code and prompts

Data control

In an enterprise‑grade configuration, you can typically:

• Ensure code indexing and embeddings stay inside your cluster or VPC
• Restrict data sent to external LLMs (prompt redaction, exclusion of sensitive repos)
• Maintain full control over backups, retention, and deletion

Driver AI deployment models

Driver AI’s private deployment story usually includes options such as:

1. Fully managed SaaS

   • Simplest deployment; Driver AI hosts everything
   • Your code may be synced or indexed in their cloud
   • Not ideal for strict data‑sovereignty requirements, but suitable for many SaaS‑oriented orgs

2. Private cloud / VPC deployment (enterprise)

   • Driver AI components deployed into your dedicated:
     • AWS/GCP/Azure VPC
   • Often supports:
     • Private networking (VPC peering, PrivateLink, etc.)
     • Restricted egress policies
   • Better fit for enterprise security and compliance requirements

3. Model routing options

   • Ability to choose specific LLM providers (OpenAI, Anthropic, Azure OpenAI, etc.)
   • Potential for connecting your own model endpoints so data stays within your ML infrastructure
   • Controls to opt out of training on your data

Data control

Key factors to confirm in a Driver AI security review:

• Whether repository contents and embeddings are stored only in your environment (for private/VPC setups)
• How long prompts and outputs are stored
• Whether data is used for cross‑customer model training (and how to disable this)
• AES‑256 at rest and TLS 1.2+ in transit
• Options for region‑specific deployments for data residency

Deployment summary

• Sourcegraph Cody: Longstanding, robust self‑host and VPC deployment options, with fine‑grained control over LLM providers and data egress. Strong choice for security‑sensitive environments that already trust Sourcegraph.
• Driver AI: Offers private/VPC deployment options at enterprise tiers and configurable model routing, but you should carefully validate storage locations, egress, and training‑data policies.

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Data privacy and training on your code

AI tools are only as safe as their data‑handling practices. You should explicitly evaluate how each product:

• Stores and encrypts your code, prompts, and responses
• Uses (or does not use) your data for model training
• Handles retention and deletion requests
• Implements tenant isolation

Sourcegraph Cody

Typical enterprise protections include:

• No training on your private code unless you explicitly opt in
• Tenant isolation: your code and embeddings are scoped to your instance
• Encryption
  • At rest: AES‑256 or equivalent
  • In transit: TLS 1.2+ or higher
• Configurable retention
  • Ability to limit retention of chat histories and logs
  • Options to disable long‑term logging of prompt content
• Data processing agreements (DPAs) and support for standard compliance frameworks (SOC 2, ISO 27001, etc.), inherited from the Sourcegraph platform

Driver AI

Driver AI’s privacy stance typically aims to reassure enterprise customers by offering:

• Opt‑out of training on your proprietary code and prompts for shared models
• Configurable retention windows for prompts and responses
• Per‑tenant data isolation, especially in VPC/private deployments
• Encryption in transit and at rest
• DPAs and compliance documentation for enterprise buyers

Where you should probe more deeply:

• Whether any subset of your data is used for analytics or product improvement and how it’s anonymized
• Exactly what content is stored for log and monitoring purposes
• Third‑party processors used (cloud providers, model providers, logging services) and their jurisdictions

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Governance, policy, and controls at scale

Beyond SSO/SCIM, RBAC, logs, and deployments, security teams also care about how easy it is to enforce consistent governance across thousands of developers.

Sourcegraph Cody governance

Because Cody is integrated into a broader code‑search and intelligence platform, you get:

• Central policy configuration
  • Turn Cody on/off organization‑wide
  • Restrict to certain groups/teams
  • Control which repos are indexed for AI
• Alignment with existing DevSecOps tooling
  • Sourcegraph’s ecosystem integrates with CI/CD and security scanning
  • Cody’s suggestions can be layered on top of existing review and compliance workflows
• Version control over config
  • Infrastructure‑as‑code or config‑as‑code approaches for your Sourcegraph deployment
  • Repeatable, auditable changes to AI settings and access

Driver AI governance

Driver AI’s governance features may include:

• Organization‑wide policy settings
  • Default permissions and feature enablement for new users
  • Restrictions on external integrations
  • Global toggles for capabilities like code generation or external model usage
• Team or workspace‑level controls
  • Different settings per department or project (e.g., more restrictive for regulated teams)
• Policy documentation and admin dashboards
  • Clear visibility into who has access to what
  • Ability to enforce consistent onboarding/offboarding rules

Governance summary

• Sourcegraph Cody: Strong, centrally managed governance built on top of a mature code‑intelligence platform, ideal if Sourcegraph is (or will be) a core engineering system of record.
• Driver AI: Offers governance controls appropriate for many enterprises, but you should verify feature parity with your internal policy needs and scale requirements.

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How to choose: Driver AI vs Sourcegraph Cody for security

When comparing Driver AI vs Sourcegraph Cody for security—specifically SSO/SCIM, RBAC, auditability, and private deployment options—base your decision on your current stack and risk tolerance.

Choose Sourcegraph Cody if:

• You already use or plan to use Sourcegraph as a central code‑search platform
• You need deep RBAC linked tightly to repository permissions
• You require proven self‑hosted or VPC deployments with extensive enterprise references
• You rely heavily on SIEM integration and detailed audit logs
• You want a single unified platform for search, code intelligence, and AI assistance, governed by the same security model

Choose Driver AI if:

• You want a flexible AI coding assistant that can integrate with your existing repo hosts and model providers
• You’re comfortable with a private cloud/VPC enterprise deployment rather than fully on‑prem (where supported)
• Your identity and governance needs are strong but not extremely bespoke, and Driver AI’s SSO/RBAC features match them
• You value rapid iteration on AI capabilities and can work closely with Driver AI on roadmap and security features

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Practical evaluation checklist

For a rigorous security review of Driver AI vs Sourcegraph Cody, have your security, IT, and engineering leaders walk through:

1. SSO & SCIM

   • Does the product support our IdP (Okta, Azure AD, etc.) via SAML/OIDC?
   • Is SCIM supported for user and group lifecycle?
   • Can we map IdP groups cleanly to product roles?

2. RBAC

   • Can we restrict access by org, team, and repository?
   • Are there controls over specific AI features (chat, completion, code generation)?
   • Does the tool enforce least‑privilege aligned with our existing repo permissions?

3. Auditability

   • Are all logins, permission changes, and admin actions logged?
   • Can logs be exported to our SIEM?
   • How long are logs retained, and can we customize retention?

4. Deployment and data residency

   • Do we have the option for self‑hosted or VPC deployment?
   • In which regions can we deploy?
   • What external endpoints (LLMs, third‑party services) does the solution call?

5. Data privacy and training

   • Is our data used to train shared models by default, and can we opt out?
   • How are code, prompts, and responses stored, encrypted, and deleted?
   • Are there certifications and DPAs that match our compliance requirements?

6. Governance and scale

   • Can we enforce global policies across the organization?
   • Is there an admin dashboard for visibility and reporting?
   • How does the solution behave during M&A, reorgs, or large‑scale staff changes?

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Final thoughts

Both Driver AI and Sourcegraph Cody can be deployed in security‑conscious environments, but they represent different starting points:

• Sourcegraph Cody is ideal if you want enterprise‑grade SSO/SCIM, highly granular RBAC, rich auditability, and mature private deployment options tightly integrated with a proven code‑intelligence platform.
• Driver AI is attractive if you want a versatile, AI‑first assistant with configurable model routing and private/VPC deployment options, and your security requirements can be satisfied by its current enterprise features.

For the most accurate, up‑to‑date details—especially around SSO/SCIM, RBAC depth, and private deployment options—request current security whitepapers and architecture diagrams from both vendors, and run a proof of concept under your security policies before committing.