AI agents are moving from demos into real work. They are reading calendars, touching codebases, pulling data from internal tools, and making decisions across live systems. That shift is exciting, but it also creates a problem that many companies are only starting to understand. The more capable AI agents become, the more risky it gets to give them broad access without strong controls.
That is where Srikar Dandamuraju and Alter enter the picture.
Alter was built around a simple but increasingly urgent idea: if AI agents are going to operate inside real businesses, they need the same kind of serious security and access control that companies expect everywhere else. Instead of treating agent security like an afterthought, Alter puts it at the center of the workflow. That focus has helped position the company as a rising name in a category that is only becoming more important.
What makes the story interesting is that this is not a founder chasing a trend from the outside. Srikar Dandamuraju came into Alter with experience in high-stakes infrastructure and enterprise systems, which gave him a practical view of what can go wrong when access, permissions, and controls are not handled carefully. That background matters, because Alter is solving a problem that sits right at the intersection of AI speed and enterprise caution.
Who Is Srikar Dandamuraju
Srikar Dandamuraju is the co-founder and CEO of Alter, a startup focused on zero-trust identity and access control for AI agents. Before building Alter, he worked in roles that exposed him to the kind of infrastructure challenges most early startup founders never see up close. His background includes Goldman Sachs, Addepar, and Clear Street, which gives him a strong foundation in systems that need to be secure, reliable, and built for environments where mistakes can carry real consequences.
That kind of experience tends to shape how a founder sees opportunity. Some entrepreneurs start by looking for what is popular. Others start by looking for what is broken. Srikar’s path looks much more like the second one. When you spend time around complex platforms, regulated systems, and enterprise-grade infrastructure, you develop a sharp sense for weak points. You notice where control breaks down, where teams rely on overly broad permissions, and where speed can quietly outpace safety.
That perspective helps explain why Alter is not positioned as just another AI tool. It is built as infrastructure for a new kind of operational reality.
How Srikar Dandamuraju Identified a Real Problem in AI Agent Security
The rise of AI agents has created a gap that many businesses are only now beginning to face. Companies want agents to take actions, not just generate text. They want them to check alerts, review logs, update tickets, read documents, access customer systems, work with GitHub, connect to Slack, and trigger actions across SaaS platforms.
But once an agent starts doing real work, the question changes from what it can do to what it should be allowed to do.
That is the heart of the problem Alter is trying to solve.
In many organizations, access control was never designed with AI agents in mind. Permissions are often too broad. Credentials can live too long. Audit trails are messy. Teams may know a request happened, but not always which agent made it, on whose behalf it acted, or whether the action should have been allowed in the first place. For human employees, companies already understand the need for role-based access, identity checks, logging, and policy enforcement. As AI agents step into the loop, those same requirements become essential again.
Srikar Dandamuraju seems to have recognized that this was not a small technical nuisance. It was a foundational problem. If companies cannot trust the access layer around AI agents, they will hesitate to put those agents into production. That means security is not just a protective layer. It is an adoption layer.
Why Alter Entered the Market at the Right Time
Timing matters in startups, and Alter arrived at a moment when the market was becoming ready for exactly this kind of product.
For a while, most of the attention in AI was centered on model quality, interface design, and workflow automation. Security was part of the conversation, but it was rarely the headline. That has started to change as more companies move beyond experimentation and begin asking how agentic systems can be used inside real operations.
Once AI agents are given access to calendars, payment tools, internal dashboards, data platforms, code repositories, or customer systems, the stakes rise fast. Suddenly, the conversation is no longer about convenience alone. It becomes about authorization, accountability, least-privilege access, runtime guardrails, and audit visibility.
That is why Alter feels well-timed. It is not trying to bolt security onto an already crowded category. It is addressing a pain point that becomes more obvious as AI agents become more capable. In that sense, Alter benefits from a broader market shift. The more serious companies become about agent workflows, the more they need a product that can help them manage trust and control.
What Alter Actually Does
Alter is designed as a zero-trust identity and access control platform for AI agents. In simple terms, it sits between an agent and the tools or APIs that the agent wants to use. Its job is to verify identity, check permissions, apply policy, manage credentials, and log what happens.
That may sound technical, but the core value is easy to understand. Alter helps companies avoid the dangerous habit of giving agents broad, long-lived access and hoping nothing goes wrong.
Zero-trust identity and access control for AI agents
At the center of Alter’s product is the idea that every request should be verified, not assumed safe just because it comes from a known system. That is what gives the platform its zero-trust character. Instead of treating an agent as permanently trusted once it has access, Alter evaluates requests in context.
Fine-grained authorization instead of blanket permissions
One of the stronger parts of Alter’s positioning is its focus on fine-grained authorization. That matters because many real risks do not come from total system compromise. They come from smaller actions that should never have been allowed in the first place. A query might reach the wrong data. A command might exceed policy limits. A workflow might attempt an action outside its role.
Fine-grained control makes it possible to be more precise. Rather than saying an agent can use a tool in general, companies can get more specific about what it can do, under what conditions, and with what limits.
Ephemeral credentials and secure credential management
Alter also leans into secure credential handling, which is a major issue in modern systems. Long-lived secrets create risk because they can be leaked, overused, forgotten, or shared too widely. A more secure model is to issue narrow, short-lived credentials tied to a specific scope or task.
That approach fits well with AI agents, which often need access in a moment, for a purpose, rather than permanent access without boundaries.
Audit trails and visibility
For enterprise teams, visibility matters almost as much as prevention. It is not enough to block unsafe activity. Companies also need to understand what happened, what was requested, who initiated it, and how the system responded. Alter’s audit-oriented positioning matters here because trust in AI systems is often built through traceability.
A product that can show the chain of action clearly becomes much easier to defend inside compliance reviews, internal security discussions, and production rollouts.
How Srikar Dandamuraju Built Alter Around Enterprise Needs
One reason Alter stands out is that it feels shaped by enterprise reality rather than startup theater.
A lot of AI products are sold on speed alone. They promise faster execution, lower effort, and more automation. Those benefits matter, but enterprise buyers rarely stop there. They also care about oversight, policy enforcement, compliance readiness, access governance, and operational safety.
That is where founder-market fit becomes important.
Srikar Dandamuraju’s background gave him exposure to environments where reliability and control are not optional. That likely influenced the way Alter was built and described from day one. Instead of framing the company around vague AI transformation language, Alter focuses on security architecture, identity, access, authorization, audit logs, and least-privilege execution.
That kind of positioning usually comes from founders who understand how buyers think once systems move from experimentation into production. It suggests that Alter is not only trying to appeal to developers building agents. It is also speaking to security leaders, infrastructure teams, and organizations that need assurance before they approve wider adoption.
The Role of Y Combinator in Alter’s Rise
Alter’s inclusion in Y Combinator’s Summer 2025 batch is another meaningful part of the company’s early momentum.
Y Combinator does not guarantee long-term success, but it does offer a strong signal that a startup is working on a relevant problem with a team that investors and operators believe is worth backing. For a young company like Alter, that kind of validation matters. It gives the startup visibility, credibility, and access to a wider network of builders, customers, and supporters.
In Alter’s case, YC also reinforces the idea that AI agent security is not a niche concern. It is a serious category in the making. When a startup built around authorization, identity, and access control for AI agents earns that level of early recognition, it says something about where the broader market is heading.
For Srikar Dandamuraju, YC likely helped accelerate awareness around a company that was already solving a timely and high-value problem.
What Makes Srikar Dandamuraju’s Approach Stand Out
Not every founder in AI is building from the same place. Some are focused on distribution. Some are focused on model wrappers. Some are trying to win attention with speed and novelty. Srikar Dandamuraju’s approach stands out because it appears grounded in infrastructure thinking.
That makes a difference.
Security startups often succeed when they solve a problem that technical teams already know they have, but have not solved well enough yet. Alter fits that pattern. It is not inventing concern about agent security. It is responding to concern that is already growing inside organizations.
There is also something smart about building around trust rather than hype. AI has no shortage of flashy products, but long-term value often comes from the layers that make adoption safer and more manageable. If companies are going to let agents interact with production systems, sensitive data, and important workflows, they will need a stronger control plane. Alter is aiming to become part of that layer.
That is a strong strategic position for an early startup, and it helps explain why Srikar Dandamuraju and Alter are getting attention.
How Alter Fits Into the Future of AI Agent Workflows
It is still early for AI agents, but the direction is becoming clearer. Businesses want systems that can do more than answer prompts. They want software that can coordinate tasks, interact with tools, retrieve information, make limited decisions, and complete work across platforms.
As that shift continues, the demand for secure orchestration will likely grow with it.
That puts Alter in an interesting spot. The company is not trying to replace agents or compete with every AI platform in the stack. Instead, it is building the trust layer around them. That layer could become increasingly valuable as more companies move from testing agents in isolated environments to deploying them in workflows that touch real users, business logic, and sensitive systems.
If that future plays out, the companies that help make AI agents safer, more accountable, and easier to govern may become some of the most important infrastructure businesses in the category.
Key Lessons From Srikar Dandamuraju and Alter
There are a few clear lessons in the story of Srikar Dandamuraju and Alter.
First, strong startups often come from identifying a problem that becomes more urgent as technology evolves. Alter is solving an issue that grows more important as AI agents gain real authority inside business systems.
Second, founder background can shape product clarity. Srikar Dandamuraju did not need to invent a complicated narrative to justify Alter. His experience in infrastructure-heavy environments already points naturally toward the kind of company he chose to build.
Third, security is often one of the biggest unlocks for adoption. Companies may be excited about AI agents, but excitement alone does not lead to production rollout. Trust does.
Finally, clear positioning matters. Alter is not trying to be everything in AI. It is focused on a very specific layer of the stack, and that clarity makes the company easier to understand, easier to remember, and easier to take seriously.
For an early-stage startup, that is a strong place to be.
