The enterprise question isn’t capture. It’s control.
On a Tuesday, the first enterprise question is usually not “can you capture AI code?” It’s “who can see the records, how long do they live, and what happens when a policy blocks a change?”
That’s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.
The useful thing here is not another dashboard. It’s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.
I keep seeing AI governance tools start with “visibility,” then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.
What would your team need first: SSO, retention, or a review policy that developers will actually use?
Replies
@praveen62 What become the first priority for most teams, SSO or retention?
Lineage Lens
@_elijah_joseph From the conversations I’ve had so far, SSO usually comes first because identity is the entry point for trust in enterprise environments. Teams want to know who accessed what before they think about long-term lifecycle rules.
But retention becomes important very quickly after that, especially once legal, compliance, or platform teams realize these records may need to exist for months or years.
This makes sense. Teams need clear ownership. Which part was hardest to build? @praveen62
Lineage Lens
@sanjau_sanjay Honestly, keeping lifecycle semantics consistent across layers has been one of the hardest parts so far. Capturing events is relatively straightforward compared to making sure the extension, backend, and policy layer all describe the exact same event state in a deterministic way.
Once those meanings drift even slightly, trust in the provenance chain starts degrading surprisingly fast.
Amazing way to explain enterprise needs. Do smaller teams care about this too?
Lineage Lens
@tessa_lynch I think smaller teams care later, but they eventually run into the same problems once AI usage becomes shared instead of individual. Early on, speed matters most. But the moment multiple developers, agents, or workflows start interacting with the same AI-generated changes, questions around ownership, review, and traceability appear surprisingly quickly.
The scale is smaller, but the coordination problem is very similar.
Good discussion. Which matters more in real use, retention or permissions? @praveen62
Lineage Lens
@carolina_ellen In practice, permissions usually matter first because they define the immediate trust boundary around the records. Teams want to know who can view, approve, export, or modify provenance data before thinking about long-term storage rules.
But retention becomes critical once organizations start treating those records as operational or compliance evidence instead of temporary telemetry.
Lineage Lens
Drop any questions below!
@praveen62 This feels right , governance only works when policy , identity , and retention are embedded in the same system as the record, not layered around it.
Lineage Lens
@daniel_scott13 Exactly. That separation is where a lot of governance systems start weakening. If policy, identity, and retention exist outside the provenance layer, the audit trail becomes dependent on surrounding infrastructure staying perfectly aligned.
Embedding governance semantics directly alongside the record makes the history itself enforceable instead of merely observable.
@praveen62 Have compliance requirements shaped your roadmap significantly?
Lineage Lens
@rowan_elizabeth1 A lot more than I expected initially. Early on I thought provenance was mostly an engineering visibility problem, but enterprise conversations quickly shifted toward retention boundaries, auditability, export controls, and access scoping.
What changed the roadmap most was realizing compliance teams do not just want logs — they want governance semantics attached to those logs in a way they can actually operationalize.
@praveen62 How do teams handle policy violations once they're detected?
Lineage Lens
@elodie_harper One thing I’m trying to avoid is treating policy violations as isolated alerts. In practice, teams usually need a full review chain around the event: what prompt triggered it, what model/tool touched it, who approved or rejected it, and whether the change was eventually applied or blocked.
Otherwise violations become disconnected notifications instead of governable workflow history.
Capture is the easy part. If the record can't explain why a blocked change was blocked and whether the next retry is actually new, the dashboard is mostly theatre. How are you handling retry admission when policy and provenance disagree?
Lineage Lens
@keesan12 That boundary is one of the harder problems I’ve been thinking about. My current view is that retries should not automatically inherit legitimacy from the original event or get treated as entirely independent actions either.
If a policy blocks a change, the retry path needs provenance continuity: what changed between attempts, whether the prompt/model/context materially shifted, who initiated the retry, and whether the policy conflict was actually resolved versus merely rephrased around.
Otherwise governance systems end up tracking retries as disconnected events while developers experience them as the same operational intent.
I'd probably start with retention and review policy before SSO. SSO matters but the bigger questions what teams keep, who reviews it and how developers can follow the policy without adding too much friction to their workflow.
Lineage Lens
@busra_seker1 That ordering makes a lot of sense honestly. Once teams begin storing prompts, generated outputs, and workflow decisions, retention and review quickly become operational concerns rather than optional governance layers.
I also think your point about workflow friction is critical. Policies only work long term if developers can realistically follow them inside normal engineering flow instead of treating them as separate compliance overhead.
This feels closer to audit infrastructure than a normal AI coding tool, which is probably the right direction.
Lineage Lens
@lucy_bennett1 That’s honestly much closer to how I’ve started thinking about it too. The deeper I go into provenance and governance, the less it feels like a traditional AI coding assistant problem and the more it resembles audit infrastructure for autonomous systems.
The difficult part is not generation anymore — it’s creating a trustworthy operational history around generation.