u/Money_Rub_7968

Hi everyone,

For people working in GRC, compliance, security, legal, or risk: I’m trying to understand how organizations are handling AI governance in practice, especially when teams start using AI agents, copilots, or LLM-based workflows.

A few questions I’m trying to think through:

• Who usually owns AI governance inside an organization?
• Is it compliance, legal, security, risk, product, engineering, or a dedicated AI governance team?
• At what point do GRC teams get involved: before deployment, during implementation, or only after an incident/audit concern?
• Are teams thinking about AI outputs/actions in real time, or mostly relying on policies, training, and after-the-fact audit logs?
• For regulated industries, what would make AI governance feel urgent enough to prioritize?

I’m technical, not a GRC practitioner, so I’m trying to learn how this works from the buyer/operator side rather than assume the org chart.

If anyone has experience with AI governance, model risk, compliance operations, or regulated AI deployments, I’d really appreciate your perspective. Feel free to comment or PM me.

Hi everyone,

I’m a technical founder working on a runtime safety/governance layer for AI agents, copilots, and LLM workflows.

The problem I’m focused on is this: a lot of AI safety and governance happens after the fact. The model or agent has already drafted the email, updated a record, made a recommendation, triggered a tool, or pushed something downstream — and only then do people think about safety, compliance, or auditability.

I’m exploring a more runtime-oriented approach: before an AI output/action reaches users or systems, it can be checked, blocked, redacted, escalated, or logged.

I’m strong technically, but I’m not a business/sales person. I’m trying to understand where the first serious users or pilot customers for something like this would realistically come from.

For people in AI safety, governance, evals, or agent deployment:

• Who actually feels this pain first?
• AI agent startups?
• Enterprise AI teams?
• Security teams?
• Compliance/legal teams?
• Regulated SaaS companies?
• Consultants building agents for clients?
• Public sector or GovCon teams?

Also, if someone here has experience taking AI safety/governance tools to market, or wants to discuss a possible partnership, I’m open to talking anytime.

Not trying to pitch blindly — I’m looking for honest advice on where technical founders should look for the first real customer in this space.

Feel free to PM me if you have advice, feedback, or partnership ideas.

Hi everyone,

I’m working on a runtime governance/compliance layer for AI agents, copilots, and LLM workflows, and I’m trying to validate whether this is a real pain point for teams building agents.

The problem I’m exploring: a lot of AI governance happens too late. The agent has already drafted the email, updated a record, made a recommendation, triggered a tool, or sent something downstream — and only then does someone think about compliance, audit, or risk.

The idea is to put a policy check before that point, so risky outputs/actions can be blocked, redacted, escalated, or logged before they reach users or systems.

For people building or deploying AI agents:

• Is runtime policy/compliance checking actually a pain point?
• Who owns this problem in your org: engineering, security, compliance, legal, or product?
• Would early buyers be agent startups, enterprise AI teams, regulated SaaS companies, or consultants building agents for clients?
• Where would you look for the first pilot customer?

Not trying to promote — I’m looking for honest feedback from people building in the agent space.

If anyone has experience with agent governance, AI safety, compliance, or regulated deployments, feel free to PM me.

Hi everyone,

I’m the founder of Reglint LLC, a Virginia-based startup building runtime AI governance and compliance controls for AI agents, copilots, and LLM workflows.

Reglint helps regulated teams block, redact, escalate, and audit risky AI outputs before they become emails, records, decisions, approvals, or downstream system actions.

We’re preparing for GovCon and partner-led opportunities:

• Virginia LLC
• AWS Marketplace seller setup mostly complete
• AWS Partner Central profile published
• MVP ready for pilot discussions
• focused on AI governance, compliance, cloud security, and regulated AI workflows

I’m trying to understand the most realistic path to a first GovCon-related customer or partner.

For people with GovCon experience, would you recommend starting with:

• prime contractors as a subcontractor
• AWS public sector/cloud partners
• APEX Accelerator introductions
• state/local opportunities
• SBIR/STTR
• direct agency outreach
• small paid pilot through AWS Marketplace/private offer

I’m not trying to pitch blindly. I’m looking for practical advice on how a new vendor actually breaks into this market.

If anyone works with primes, AWS public sector partners, AI governance, responsible AI, or compliance opportunities, feel free to PM me or reach out through the contact page on reglint.ai.