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Introduction
The autonomous AI agent you just deployed to automate customer support, query production databases, or manage cloud infrastructure is now your organization’s newest—and most invisible—security threat. Forrester has officially named AI agent threats the top CISO risk for 2026, warning that personal AI agents are infiltrating enterprises via browser hooks and inbox access, operating as “shadow operators” that access data at machine speed outside any security governance framework. With non-human identities (NHIs) now outnumbering human users in most enterprise environments—in some cases by a factor of 25 to 50—the traditional identity and access management (IAM) stacks built for human session flows are completely unequipped to govern these autonomous, nondeterministic actors.
Learning Objectives
- Understand why AI agents represent a fundamentally new class of identity risk that breaks traditional IAM models
- Master practical techniques for discovering, classifying, and governing non-human identities across cloud and on-premises environments
- Implement zero-trust principles, dynamic token exchange, and least-privilege access controls for agentic systems
- Learn to audit, monitor, and remediate NHI-related vulnerabilities using open-source tools and cloud-1ative controls
You Should Know
- The NHI Discovery Crisis: Finding What You Didn’t Know Existed
Most security teams cannot answer a simple question: How many AI agents are operating in your environment right now? According to the Cloud Security Alliance (CSA), organizations must begin by identifying NHIs across cloud platforms, SaaS applications, on-premises systems, container environments, deployment pipelines, and AI platforms. This inventory must include the identity subject, associated credentials, purpose, permissions, dependencies, and business context.
When organizations run a first NHI discovery exercise, they don’t find a manageable handful—they find hundreds, distributed across cloud tenants, SaaS platforms adopted without central IT oversight, development and staging environments never decommissioned, and third-party integrations that fell outside every prior access review cycle.
Practical Discovery Commands:
Linux/macOS – Discover service accounts and their permissions:
List all system service accounts cat /etc/passwd | grep -E "/(bin|sbin|false|nologin)" | cut -d: -f1 Find cron jobs running under service accounts for user in $(cat /etc/passwd | grep -E "/(bin|sbin|false|nologin)" | cut -d: -f1); do crontab -u $user -l 2>/dev/null done Audit sudo privileges for non-human accounts grep -r "NOPASSWD" /etc/sudoers /etc/sudoers.d/
AWS – Discover machine identities and unused credentials:
List all IAM users and identify those with console access disabled (likely machine accounts)
aws iam list-users --query 'Users[?PasswordLastUsed==null]'
Find access keys older than 90 days
aws iam list-access-keys --user-1ame <USER> --query 'AccessKeyMetadata[?CreateDate<=<code>2026-01-01</code>]'
Identify roles with overly permissive policies
aws iam list-roles --query 'Roles[?AssumeRolePolicyDocument.Contains(<code>"Principal":{"AWS":""}</code>)]'
Azure – Discover service principals and managed identities:
List all service principals
Get-AzADServicePrincipal | Select-Object DisplayName, AppId, AccountEnabled
Find service principals with credentials older than 90 days
Get-AzADServicePrincipal | ForEach-Object {
$creds = Get-AzADAppCredential -ApplicationId $<em>.AppId
if ($creds.EndDate -lt (Get-Date).AddDays(-90)) {
Write-Output "$($</em>.DisplayName) has credentials expiring soon"
}
}
List managed identities across subscriptions
Get-AzResource -ResourceType "Microsoft.ManagedIdentity/userAssignedIdentities"
GCP – Discover service accounts and their permissions:
List all service accounts gcloud iam service-accounts list Get IAM policy for each service account for sa in $(gcloud iam service-accounts list --format="value(email)"); do gcloud projects get-iam-policy $PROJECT_ID --flatten="bindings[].members" \ --format="table(bindings.role)" --filter="bindings.members:$sa" done
Why this matters: Without a complete inventory, you cannot govern what you cannot see. Every undiscovered agent is a potential backdoor operating with whatever permissions were assigned at creation—permissions that rarely get reviewed or revoked.
- The Shared API Key Problem: Static Credentials Are the New Shadow IT
Developers are deploying AI agents at breakneck speed, and to get them running quickly, teams often rely on a dangerous shortcut: generating a single, shared API key and pasting it into a configuration file. This practice turns shared API keys into the new shadow IT—stripping away visibility and accountability. Most AI agents today operate using static API keys with broad, long-lived permissions, meaning that if an attacker tweaks the agent via prompt injection, they could gain possession of admin keys.
A shared key lacks context—the system knows the API token was used, but not why or by whom the agent was triggered. If an agent misbehaves, alters a database incorrectly, or leaks sensitive data, a traditional security stack cannot determine who or what initiated the call—it only sees the key.
The Rotation Nightmare: Nobody rotates static keys as often as they should because it’s a logistical nightmare. Rotating a key hardcoded into three different microservices, a CI/CD pipeline, and a local `.env` file requires generating a new key, finding every repo using the old key, redeploying all services, and hoping no cron job was missed. I’ve seen production keys that haven’t been rotated in years because the original developer who created them left, and no one wants to be the one to break the build.
Practical Remediation – Migrate from Static Keys to Dynamic Tokens:
Step 1: Audit existing static credentials
Find hardcoded secrets in code repositories grep -r "API_KEY|SECRET|TOKEN" --include=".env" --include=".yaml" --include=".json" . Use truffleHog for secret scanning trufflehog filesystem . --only-verified Check for AWS keys in environment variables ps aux | grep -E "AWS_ACCESS_KEY_ID|AWS_SECRET_ACCESS_KEY"
Step 2: Implement OAuth 2.0 On-Behalf-Of (OBO) Flow
Instead of static keys, shift to dynamic token exchange (RFC 8693) where the agent acts as a delegate rather than a God Mode administrator:
Example: Agent requesting scoped token on behalf of user
import requests
from oauthlib.oauth2 import BackendApplicationClient
from requests_oauthlib import OAuth2Session
Agent authenticates itself
agent_client = OAuth2Session(client_id=AGENT_CLIENT_ID,
client_secret=AGENT_CLIENT_SECRET)
agent_token = agent_client.fetch_token(token_url=TOKEN_ENDPOINT)
Request OBO token for specific user with scoped permissions
obo_payload = {
"grant_type": "urn:ietf:params:oauth:grant-type:token-exchange",
"subject_token": agent_token['access_token'],
"subject_token_type": "urn:ietf:params:oauth:token-type:access_token",
"actor_token": user_session_token,
"actor_token_type": "urn:ietf:params:oauth:token-type:access_token",
"scope": "read:customer-data write:tickets",
"audience": "api://customer-service"
}
obo_response = requests.post(TOKEN_ENDPOINT, data=obo_payload)
scoped_token = obo_response.json()['access_token']
Step 3: Use short-lived credentials with automatic rotation
AWS: Use instance metadata service for temporary credentials curl -s http://169.254.169.254/latest/meta-data/iam/security-credentials/ Azure: Use managed identity for token acquisition curl 'http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https://management.azure.com/' -H Metadata:true GCP: Use workload identity federation gcloud auth login --cred-file=/path/to/workload-identity-config.json
- The Model Context Protocol (MCP): Standardizing Agent Identity
The Model Context Protocol (MCP) is emerging as the standard for AI agents to request and receive scoped access tokens. Instead of embedding static API keys, MCP enables agents to authenticate themselves and request permission to act on behalf of a specific user with a temporary, scoped access token.
Implementing MCP with Okta MCP Server:
Install and configure Okta MCP Server npm install -g @okta/mcp-server Configure MCP server with Okta credentials export OKTA_ORG_URL="https://your-org.okta.com" export OKTA_API_TOKEN="your-scoped-api-token" Start MCP server okta-mcp-server --port 3000 --config ./mcp-config.json
MCP Server Configuration (mcp-config.json):
{
"identity_providers": [
{
"type": "okta",
"org_url": "https://your-org.okta.com",
"client_id": "your-client-id",
"scopes": ["openid", "profile", "email", "offline_access"]
}
],
"agent_policies": [
{
"agent_id": "customer-support-agent",
"allowed_scopes": ["read:customer-data", "write:tickets"],
"max_session_duration": 3600,
"require_human_approval": ["write:tickets", "delete:customer-data"]
}
],
"audit": {
"enabled": true,
"log_level": "info",
"output": "stdout"
}
}
Agent requesting access via MCP:
Agent uses MCP to request scoped access
import json
import requests
mcp_request = {
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_access_token",
"arguments": {
"user_id": "user-123",
"requested_scopes": ["read:customer-data", "write:tickets"],
"purpose": "Customer ticket update requested by user"
}
}
}
response = requests.post("http://localhost:3000/mcp", json=mcp_request)
token = response.json()['result']['access_token']
- Zero Trust for Agentic Systems: Least Privilege by Design
Applying zero trust principles to AI agents means every agent and tool must have a verifiable identity, dynamic secrets, and least-privilege access. The OWASP GenAI Security Project has catalogued that three of the four highest-rated risks are identity questions: tool misuse and exploitation, identity and privilege abuse including delegated and inherited trust, and rogue agents that act outside their intended behavior.
Practical Implementation – CIEM for Non-Human Identities:
Cloud Infrastructure Entitlement Management (CIEM) solutions discover and right-size excessive permissions across AWS, Azure, and GCP for both human and non-human identities.
AWS – Audit and remediate overprivileged roles:
Find roles with wildcard permissions aws iam list-roles --query 'Roles[?AssumeRolePolicyDocument.Contains(<code>"Action":""</code>)]' Use IAM Access Analyzer to find unused permissions aws accessanalyzer list-findings --analyzer-arn <ARN> Generate last accessed info for a role aws iam generate-service-last-accessed-details --arn <ROLE_ARN> Get policy summary aws iam get-policy --policy-arn <POLICY_ARN> Attach least-privilege policy aws iam attach-role-policy --role-1ame <ROLE> --policy-arn arn:aws:iam::aws:policy/job-function/ViewOnlyAccess
Azure – Review and remediate service principal permissions:
Get role assignments for service principals
Get-AzRoleAssignment | Where-Object { $_.ObjectType -eq "ServicePrincipal" }
Find service principals with Owner or Contributor roles
$dangerousRoles = @("Owner", "Contributor", "User Access Administrator")
Get-AzRoleAssignment | Where-Object {
$<em>.ObjectType -eq "ServicePrincipal" -and $dangerousRoles -contains $</em>.RoleDefinitionName
}
Remove excessive permissions
Remove-AzRoleAssignment -ObjectId <SP_OBJECT_ID> -RoleDefinitionName "Contributor" -Scope <SCOPE>
GCP – Audit service account permissions:
List all service account IAM policies
gcloud projects get-iam-policy $PROJECT_ID --format=json | \
jq '.bindings[] | select(.members[] | contains("serviceAccount:"))'
Find service accounts with primitive roles (Owner/Editor/Viewer)
gcloud projects get-iam-policy $PROJECT_ID --format=json | \
jq '.bindings[] | select(.role | contains("roles/owner") or contains("roles/editor"))'
Apply least-privilege with custom roles
gcloud iam roles create custom_viewer --project=$PROJECT_ID \
--title="Custom Viewer" \
--description="Read-only access to specific resources" \
--permissions="resourcemanager.projects.get,compute.instances.list" \
--stage=GA
5. Agent Lifecycle Governance: From Onboarding to Decommissioning
Every identity should have a designated owner accountable for its lifecycle and security posture. For AI agents, the CSA recommends both a human sponsor and an oversight owner, with ownership established during application or agent onboarding rather than added after deployment.
Practical Governance Framework:
Step 1: Agent Registration and Classification
agent-registry.yaml apiVersion: security.enterprise/v1 kind: AgentRegistration metadata: name: customer-support-agent-v1 owner: team-customer-support sponsor: [email protected] oversight: [email protected] spec: identity_type: AI_AGENT risk_level: MEDIUM allowed_scopes: - read:customer-data - write:tickets - read:knowledge-base prohibited_scopes: - delete:customer-data - admin: approval_workflow: require_human_approval: true approvers: - manager-customer-support - security-team lifecycle: max_session_duration: 3600 auto_decommission_after: 30d credential_rotation: 7d delegation: allow_sub_agents: true max_depth: 2 require_audit_trail: true
Step 2: Continuous Monitoring and Certification
Monitor agent activity for anomalies
Example: Detect unusual API call patterns
awk '{print $1, $7}' /var/log/agent-api.log | \
sort | uniq -c | sort -1r | head -20
Check for privilege drift (permissions granted beyond initial scope)
diff agent-initial-permissions.json agent-current-permissions.json
Generate access review report
python3 generate-1hi-review.py --output review-report-$(date +%Y%m).html
Step 3: Automated Decommissioning
Decommission stale agent identities (AWS example)
aws iam list-access-keys --user-1ame <AGENT_USER> --query \
'AccessKeyMetadata[?CreateDate<=<code>2026-01-01</code>]' --output table
Delete orphaned service accounts
aws iam delete-user --user-1ame <ORPHANED_USER>
Azure: Disable stale service principals
$staleSPs = Get-AzADServicePrincipal | Where-Object {
(Get-AzRoleAssignment -ObjectId $<em>.Id).Count -eq 0 -and
$</em>.AccountEnabled -eq $true
}
foreach ($sp in $staleSPs) {
Update-AzADServicePrincipal -ObjectId $sp.Id -AccountEnabled $false
}
- The Three Control Planes Every CISO Must Address
Forrester analyst Jitin Shabadu identifies three control planes needing agent-specific treatment: internal agents your organization deploys, inbound agents calling your APIs from outside, and outbound agents your employees are using to reach external services. Most IAM programs have addressed none of the three.
Securing Internal Agents:
- Treat each agent as a non-human principal with the same discipline applied to employees
- Every agent should run as the requesting user in the correct tenant, with permissions constrained to that user’s role and geography
- Prohibit cross-tenant on-behalf-of shortcuts
Securing Inbound Agent Calls:
API Gateway with agent identity validation Example: Kong API Gateway plugin for agent authentication curl -X POST http://kong:8001/plugins \ --data "name=agent-auth" \ --data "config.auth_header=X-Agent-ID" \ --data "config.allowed_agents=$(cat allowed-agents.txt | tr '\n' ',')"
Securing Outbound Agent Access:
- Implement data loss prevention (DLP) for agent outbound communications
- Use TLS inspection and egress filtering
- Log all outbound agent API calls with full context (agent ID, user session, purpose)
What Undercode Say
- The governance gap is not a technology problem—it’s a speed problem. AI agents operate at machine speed while security teams still operate at human speed. Nation-state actors are exploiting this exact dynamic, using AI to automate exploitation at scale. Your IAM tooling was designed for human session flows, not autonomous agent calls that don’t trigger MFA, don’t create parseable SIEM logs, and don’t stop when the employee goes home.
-
The “82:1 Crisis” is the biggest open door in cybersecurity history. Machine identities now outnumber human identities by staggering margins—82 to 1 in some environments. Of the 109 machine identities per human, roughly 79 are AI agents, projected to grow by 85% over the next 12 months. Every automation your platform team deploys widens this gap.
-
Static credentials are the new default admin password. We spent a decade eliminating shared passwords for humans, yet we’re handing AI agents hardcoded secrets with God Mode permissions. If a developer’s AI assistant gets prompt-injected, the attacker inherits whatever keys were in that `.env` file—often including production database credentials.
-
Identity is the substrate that supports your entire AI security strategy. Without verified agent identity, you cannot enforce fine-grained access control, maintain accurate audit logs, apply precise rate limits, or attribute costs to specific business units. Every agent touching your infrastructure must be known, authenticated, and authorized.
-
The OWASP Top 10 for Agentic Applications is your new security baseline. Three of the four highest-rated risks are identity questions: tool misuse (ASI02), identity and privilege abuse including delegated trust (ASI03), and rogue agents acting outside intended behavior (ASI10). If your security program isn’t addressing these categories, you’re not securing AI agents—you’re just hoping they behave.
Prediction
-
+1 By 2028, organizations that implement agent identity governance will see 60% fewer credential-related breaches compared to those relying on legacy IAM, as dynamic token exchange and continuous certification become standard practice.
-
+1 The emergence of standards like the Agent Identity Protocol (AIP) and Agent Trust Transport Protocol (ATTP) will create a unified framework for decentralized agent identity, enabling secure multi-agent workflows without centralized identity providers.
-
-1 The 40% of enterprises projected to embed task-specific AI agents by the end of 2026, combined with fewer than 21% having mature governance frameworks, will result in a wave of high-profile breaches traced directly to ungoverned agent identities.
-
-1 Regulatory bodies like the EU AI Act ( 15) will begin enforcing cyber-resilience and misuse resistance requirements for agentic systems, catching unprepared organizations off-guard with significant compliance penalties.
-
+1 CIEM solutions purpose-built for non-human identities will become mandatory investments, as cloud providers increasingly offer native agent identity management capabilities integrated directly into their IAM platforms.
-
-1 The “shadow agent” problem will escalate as business units continue deploying agents directly through SaaS platforms without central IT oversight, creating a distributed attack surface that traditional security tools cannot see.
-
+1 Security teams that adopt the CSA’s NHI governance model—starting with discovery, classification, and ownership before moving to automated lifecycle management—will establish the foundational controls needed to scale AI safely across the enterprise.
▶️ Related Video (76% Match):
https://www.youtube.com/watch?v=2FFYJP20abo
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