Alter

What do they actually do

Alter provides a security layer that sits between AI agents, the LLMs they call, and the external tools/APIs those agents use. It authenticates who or what is making each request, checks parameters against fine‑grained policies, injects short‑lived credentials so agents only get minimum access, blocks risky actions in real time, and records everything for audits and compliance (YC profile, site).

Teams integrate Alter with common agent frameworks (e.g., LangChain, LlamaIndex, CrewAI), route agent calls through Alter, and use role/attribute‑based policies for parameter‑level authorization. When allowed, Alter issues ephemeral credentials or refreshes OAuth tokens; when not, it blocks the call and logs the decision with context for SOC2/HIPAA/GDPR reporting. The product is in early access with a “book a call” motion and is currently piloting with enterprise/regulated teams (site, YC profile).

Who are their target customer(s)

• CISOs / InfoSec leaders at regulated companies: They must enable agent automation without data leaks or compliance violations and need controls and evidence they can show auditors to pass SOC2/HIPAA/GDPR reviews (YC profile, site).
• Engineering teams running agent workflows in production: Their agents make tool/database calls that can cause harm (e.g., deleting data, exfiltrating secrets). They need to block risky requests and enforce least‑privilege access per call (YC profile, site).
• Platform / infrastructure engineers integrating agent frameworks: They face heavy lift to add security hooks, token management, and credential rotation for each agent and tool, slowing deployments and increasing ops burden (site).
• Compliance and risk teams: They lack trustworthy, searchable logs of what agents did and why, making investigations slow and audit reporting painful (YC profile, site).
• Product/security engineers building enterprise agent features: They need real‑time guardrails (e.g., blocking harmful SQL) and short‑lived credentials so fast iteration doesn’t introduce long‑lived secrets or high‑impact mistakes (YC profile, site).

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led, paid pilots with regulated teams (finance/healthcare/government), doing hands‑on integrations and manual parameter/credential checks to ensure safe launches and gather concrete feedback (YC profile, site).
• First 50: Turn bespoke work into connector templates (LangChain, LlamaIndex, common tools) and work with a few security consultancies/IAM-SIEM integrators/MSPs to run standardized pilots at their customers (site).
• First 100: Productize with hardened deployment options (hosted/managed/on‑prem), compliance artifacts, and SDKs; add reseller/VAR agreements so partners can sell and operate Alter at scale in regulated accounts (YC profile, site).

What is the rough total addressable market

Top-down context:

Alter sits across IAM (including machine identity), API security, and AI governance—large, growing markets enterprises already fund. By 2030: IAM ≈ $42.6B, API security ≈ $4.6B, AI governance ≈ $15.8B, implying a conservative ~$63B relevant TAM (MarketsandMarkets, Mordor/industry, Forrester).

Bottom-up calculation:

Conservative 2030 TAM: IAM ($42.6B) + API security ($4.6B) + AI governance ($15.8B) ≈ $63B. Inclusive view adds small slices of adjacent DLP/eGRC spend to reach ~$70–75B (Mordor DLP, Grand View eGRC).

Assumptions:

• Do not double‑count PAM when it is included within IAM totals.
• Only a small fraction of DLP/eGRC spend is relevant to agent authorization/audit needs.
• Enterprise adoption of agent workflows in regulated sectors grows steadily through 2030.

Who are some of their notable competitors

• Zenity: Security and governance layer for AI agents (discovery, runtime guardrails, observability). Overlaps on protecting agent actions and giving security teams a control plane, positioned as broader agent governance vs. narrow authorization middleware (site).
• Aembit: Identity & access for agentic AI with just‑in‑time identities, policy‑based access, and secretless credentialing for non‑human identities—direct overlap on ephemeral credentials and agent identity lifecycle (site).
• Noma: Runtime protection and observability for AI agents (detects malicious prompts, blocks unauthorized actions, maps agent risk). Emphasizes detection/response and risk mapping alongside real‑time blocking (site).
• HashiCorp Vault: Widely used for dynamic secrets and short‑lived credentials. Not agent‑specific, but teams can build similar controls with Vault plus custom policy layers—a common in‑house alternative (site).
• Oso: Developer‑focused authorization library/platform for fine‑grained access policies. Overlaps on policy evaluation but requires customers to integrate it into agent and credential workflows rather than providing a packaged runtime gateway and audit product (site).