Traditional IAM systems govern human users accessing internal applications. But AI agents now browse the public internet — and no IAM framework exists to control their access. URL categorization provides the policy data source that makes Agent IAM possible: define what categories each agent identity can access, enforce permissions per navigation event, and audit every decision.
Organizations have spent decades building IAM for employees. AI agents browsing the open web have no equivalent identity or permission system.
When you deploy an AI agent with browser capabilities, that agent inherits unrestricted access to the entire public internet. There is no group policy, no role assignment, no permission boundary. A financial research agent and a marketing content agent have identical access levels — both can navigate to banking portals, adult content sites, government databases, and competitor internal pages. This is the equivalent of giving every employee in your organization domain admin privileges with no access controls.
Our 102 million domain database transforms the unstructured internet into a structured permission space. Every domain is tagged with IAB categories, web filtering classifications, page types, and reputation scores. These tags become the attributes that your Agent IAM system uses to define and enforce access policies. A financial research agent gets access to "Business and Finance" and "Financial Services" categories. A marketing agent gets access to "Advertising," "Social Media," and "News." Neither agent can reach "Adult," "Gambling," "Malware," or any other blocked category.
This approach extends the ABAC (Attribute-Based Access Control) model that enterprises already use for internal resources to the open web. The attributes are the domain's category, page type, and reputation score. The subject is the agent identity. The policy engine evaluates these attributes against the agent's role-based permissions on every navigation event. The result is a fully auditable IAM layer for AI agents browsing the public internet.
Three pillars of Agent Identity and Access Management powered by domain classification
Assign each AI agent a unique identity with associated metadata: the team that owns it, the task scope it serves, the risk tolerance level, and its approval chain for escalations. This identity becomes the subject in every access control decision. When the agent requests navigation to a URL, the IAM layer resolves the agent identity and retrieves its permission profile before evaluating the domain's category against the policy.
Define permission sets using IAB categories and web filtering classifications as the resource scope. A "Financial Research" role might include read access to "Business and Finance," "Financial Services," "Banking," and "Investing" categories, with explicit denies on "Gambling," "Cryptocurrency Exchanges," and "Adult." Permissions cascade through the IAB hierarchy: granting "Technology & Computing" at Tier 1 automatically includes all Tier 2, 3, and 4 sub-categories.
Every navigation event produces an access decision log entry: agent identity, target URL, resolved category, page type, permission evaluation result (allow/deny/escalate), and timestamp. These logs feed into your SIEM, enable access reviews, support compliance reporting, and provide the forensic trail for incident investigations. The log format is designed to be compatible with existing IAM audit pipelines.
Role-based access control for AI agents using domain classification as the permission layer
import http.client
import json
from datetime import datetime
class AgentIAMEvaluator:
"""Evaluates agent navigation requests against
role-based category permissions."""
AGENT_ROLES = {
"financial_researcher": {
"allowed_categories": [
"Business and Finance", "Financial Services",
"Banking", "Investing", "Insurance"
],
"blocked_page_types": ["login", "checkout", "admin"],
"risk_level": "medium"
},
"marketing_analyst": {
"allowed_categories": [
"Advertising", "Social Media", "News",
"Marketing", "Digital Marketing"
],
"blocked_page_types": ["login", "admin", "settings"],
"risk_level": "low"
}
}
def __init__(self, api_key):
self.api_key = api_key
self.conn = http.client.HTTPSConnection(
"www.websitecategorizationapi.com"
)
self.access_log = []
def classify_domain(self, target_url):
payload = (
f"query={target_url}"
f"&api_key={self.api_key}"
f"&data_type=url"
f"&expanded_categories=1"
)
headers = {
"Content-Type": "application/x-www-form-urlencoded"
}
self.conn.request(
"POST",
"/api/iab/iab_web_content_filtering.php",
payload,
headers
)
res = self.conn.getresponse()
return json.loads(res.read().decode("utf-8"))
def evaluate_access(self, agent_id, agent_role, url):
role_config = self.AGENT_ROLES.get(agent_role)
if not role_config:
return self._deny(agent_id, url, "Unknown role")
data = self.classify_domain(url)
page_type = data.get("page_type", "unknown")
categories = [
c[0].split("Category name: ")[1]
for c in data.get("iab_classification", [])
]
# Check page-type restrictions
if page_type in role_config["blocked_page_types"]:
return self._deny(
agent_id, url,
f"Page type '{page_type}' blocked for role"
)
# Check category permissions
for cat in categories:
if any(a.lower() in cat.lower()
for a in role_config["allowed_categories"]):
return self._allow(agent_id, url, cat)
return self._deny(
agent_id, url, "No matching allowed category"
)
def _allow(self, agent_id, url, category):
entry = {
"agent": agent_id, "url": url,
"decision": "ALLOW", "category": category,
"timestamp": datetime.utcnow().isoformat()
}
self.access_log.append(entry)
return entry
def _deny(self, agent_id, url, reason):
entry = {
"agent": agent_id, "url": url,
"decision": "DENY", "reason": reason,
"timestamp": datetime.utcnow().isoformat()
}
self.access_log.append(entry)
return entry
# Usage
iam = AgentIAMEvaluator(api_key="your_api_key")
result = iam.evaluate_access(
agent_id="agent-fin-001",
agent_role="financial_researcher",
url="https://bloomberg.com/markets"
)
print(result)
class AgentPermissionGate {
constructor(apiKey, roleDefinitions) {
this.apiKey = apiKey;
this.roles = roleDefinitions;
this.auditLog = [];
}
async evaluateNavigation(agentId, role, targetURL) {
const roleDef = this.roles[role];
if (!roleDef) {
return this.logDecision(agentId, targetURL, "DENY",
"Unknown agent role");
}
const response = await fetch(
"https://www.websitecategorizationapi.com" +
"/api/iab/iab_web_content_filtering.php",
{
method: "POST",
headers: {
"Content-Type": "application/x-www-form-urlencoded"
},
body: new URLSearchParams({
query: targetURL,
api_key: this.apiKey,
data_type: "url",
expanded_categories: "1"
})
}
);
const data = await response.json();
const pageType = data.page_type || "unknown";
if (roleDef.blockedPageTypes.includes(pageType)) {
return this.logDecision(agentId, targetURL, "DENY",
`Blocked page type: ${pageType}`);
}
const category = data.filtering_taxonomy?.[0]?.[0]
?.replace("Category name: ", "") || "Unknown";
if (roleDef.allowedCategories.includes(category)) {
return this.logDecision(agentId, targetURL, "ALLOW",
category);
}
return this.logDecision(agentId, targetURL, "DENY",
"Category not in permission set");
}
logDecision(agentId, url, decision, detail) {
const entry = {
agentId, url, decision, detail,
timestamp: new Date().toISOString()
};
this.auditLog.push(entry);
return entry;
}
}
Purpose-built domain databases for AI agent filtering. Includes IAB categories, 20+ page types, reputation scores, and popularity rankings. One-time purchase with perpetual license.
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One-time purchase: Perpetual license | Optional Updates: $1,599/year
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Also available: Enterprise URL Database up to 102M domains from $2,499. View all database tiers →
Search any IAB or Web Filtering category to see how many domains are in our 102M Enterprise Database — the same data your AI agent filtering rules will reference.
How 102 million domains from our main Enterprise Database are distributed across IAB v3 taxonomy classifications
Spanning Tier 1 through Tier 4 classifications from our 102M Enterprise Database
Charts display domain counts for the top 50 out of 700+ categories in our 102M Enterprise Database. To check the number of domains for the remaining 650+ categories, use the Category Counter tool above .
Identity and Access Management is the bedrock of enterprise security. Every employee has an identity, every identity has a role, every role has permissions, and every permission is audited. This framework has been refined over decades, standardized through protocols like SAML, OAuth, and SCIM, and embedded into every major enterprise software stack. Yet when organizations deploy AI agents that browse the public internet, all of this governance infrastructure disappears. The agent has no identity, no role, no permissions, and no audit trail. It is as if the entire IAM discipline was suspended the moment an HTTP request leaves the corporate network.
Extending IAM to AI agents requires solving a fundamental data problem: the public internet has no built-in access control metadata. Internal applications expose permission models through APIs. SaaS platforms define roles and scopes. But a public website does not declare "this page requires the Financial Analyst role." The only way to create a permission model for the open web is to classify it — and that is exactly what a 102 million domain categorization database provides.
Attribute-Based Access Control (ABAC) is the most natural IAM model for agent web access because it evaluates multiple attributes at decision time. The subject attributes include the agent's identity, role, team assignment, and risk tolerance. The resource attributes come from the URL categorization database: IAB category, page type, web filtering classification, and domain reputation score. The environment attributes include time of day, the agent's current task context, and whether a human supervisor is actively monitoring. The policy engine evaluates these attributes together to produce an allow, deny, or escalate decision.
This ABAC model is more flexible than simple allowlist/blocklist approaches because it enables context-dependent access decisions. A research agent might be allowed to access "Financial Services" domains during business hours but blocked outside of working hours. A customer support agent might be allowed to access "E-Commerce" domains only when processing a specific customer request. The same domain category can be allowed or denied depending on the combination of subject, resource, and environment attributes.
Just as enterprise IAM requires role engineering — defining the minimal set of permissions each job function needs — Agent IAM requires agent role engineering. This process starts by enumerating every agent deployment and its intended task scope. A financial research agent needs access to financial news sites, SEC filings, company investor relations pages, and market data providers. It does not need access to social media, entertainment, adult content, or e-commerce sites. The role definition translates this task scope into a set of allowed IAB categories and an explicit deny list for categories outside the scope.
The principle of least privilege applies directly. Every agent role should start with zero web access and add only the categories required for the agent's task. This prevents scope creep — the gradual expansion of agent access that occurs when teams add "just one more category" without reviewing the full permission set. Regular access reviews, similar to the access certification processes used for human IAM, should evaluate whether each agent's category permissions still match its actual task requirements.
In human IAM, sessions are managed through authentication tokens that expire after a defined period. Agent IAM should implement similar session management. When an agent begins a task, it receives a scoped access token that defines its category permissions for that specific task instance. The token expires when the task completes or after a maximum duration. If the agent's task scope changes mid-session — for example, pivoting from research to outreach — the agent must request a new token with the appropriate permissions.
Token scoping prevents a common attack vector: an agent that is initially authorized for narrow research being redirected (through prompt injection or adversarial web content) to perform unauthorized actions on sensitive sites. Because the token only grants access to specific categories, even a compromised agent cannot navigate outside its permitted scope.
Enterprise deployments increasingly use multi-agent architectures where multiple specialized agents collaborate on complex tasks. An orchestrator agent might delegate web research to a search agent, data extraction to a scraping agent, and analysis to a reasoning agent. Each agent in this chain needs its own identity and permissions. The search agent needs broad category access for discovery. The scraping agent needs narrow access to specific approved domains. The reasoning agent may need no web access at all.
Federated identity management allows the orchestrator to issue delegated permissions to sub-agents, similar to how OAuth allows applications to request scoped access on behalf of users. The orchestrator's own permissions bound what it can delegate — a financial research orchestrator cannot grant its sub-agents access to adult content categories that are outside its own permission set. This delegation model preserves the principle of least privilege across the entire agent chain.
Enterprise compliance frameworks — SOC 2, ISO 27001, HIPAA, GDPR, PCI DSS — all include requirements for access control and audit logging. As AI agents become part of the enterprise technology stack, these compliance requirements extend to agent behavior. SOC 2 requires that all system access is controlled and logged. If an AI agent accesses web resources without access controls and audit trails, the organization's SOC 2 compliance is compromised.
Agent IAM with URL categorization directly addresses these compliance requirements. The agent identity provides the "who." The URL and its category provide the "what." The policy decision provides the "whether." The timestamp provides the "when." Together, these fields create a complete access control audit trail that satisfies compliance frameworks' requirements for documented access management.
The most mature Agent IAM implementations go beyond binary allow/deny decisions to provide access intelligence — analytics that reveal patterns in agent browsing behavior, identify anomalies that suggest compromised agents, and recommend permission optimizations based on actual usage. If a financial research agent has permission to access 50 IAB categories but consistently uses only 8, the access intelligence layer recommends tightening the permission set to reduce the blast radius of a potential compromise.
This intelligence layer is only possible when you have structured categorization data for every navigation event. Without URL categories, agent browsing logs are just lists of URLs — unintelligible at scale. With categories, the same logs become structured access patterns that reveal which categories each agent actually needs, which categories are never accessed (and can be removed from permissions), and which unusual category access patterns might indicate a security event.
Define agent identities, assign category-based permissions, and audit every web navigation decision. 102 million domains classified for role-based agent access control.