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Introduction:
Every AI Security Assessment tells a slightly different story, but the findings are often surprisingly consistent. From shadow AI and excessive permissions to governance gaps and limited visibility, many organizations are discovering that AI adoption has outpaced their security controls. The data confirms this trend: according to IBM’s 2025 Cost of a Data Breach Report, shadow AI adds an average of $670,000 to breach costs, and 97% of AI-related breaches lacked proper access controls. With 92% of organizations lacking full visibility into their AI identities and 95% doubting they could detect misuse if it happened, the gaps are not theoretical — they are operational realities.
Learning Objectives:
- Understand the six most common AI security risks uncovered during enterprise security assessments
- Learn how to detect shadow AI, enforce least-privilege access, and close governance gaps
- Gain practical, step-by-step guidance with Linux/Windows commands and tool configurations to mitigate these risks
- Shadow AI: The Unseen Threat Already Operating in Your Environment
Shadow AI occurs when employees or business units deploy AI tools, models, or agents without involving IT or security teams. According to Delinea’s 2025 report, 44% of organizations with at least some AI usage struggle with business units deploying AI solutions without IT and security involvement. The danger is compounded by the fact that shadow AI breaches averaged 247 days to detect, and in those incidents, 65% of compromised data involved customer PII.
Step-by-Step Guide to Detecting Shadow AI:
Step 1: Discover Unauthorized AI Integrations
Start by auditing your cloud environments for unknown API connections and non-human identities. Effective shadow AI detection requires accepting a fundamental shift: you cannot block what you cannot see, and you cannot see what operates inside already-trusted boundaries.
Step 2: Scan SaaS Platforms and CI/CD Pipelines
Many AI tools are embedded within existing SaaS platforms. Audit your SaaS marketplace integrations and review CI/CD pipeline configurations for unauthorized AI features.
Step 3: Implement Continuous Discovery
Only 16% of organizations treat AI as a distinct identity, and just 7% have a dedicated AI governance team. Deploy tools that continuously discover AI tools accessing your data.
Linux Command for Network Discovery of AI Endpoints:
Discover potential AI/ML API endpoints in your environment nmap -p 443 --open -sV --script=http-title,http-headers 192.168.1.0/24 | grep -i "ai|openai|anthropic|cohere|huggingface" Monitor outbound connections to known AI service IP ranges sudo tcpdump -i eth0 -1 'dst net 104.18.0.0/16 or dst net 34.64.0.0/16' -c 100
Windows PowerShell Command for Monitoring AI Tool Usage:
Check for recently installed AI-related software
Get-WmiObject -Class Win32_Product | Where-Object { $_.Name -match "AI|ChatGPT|Copilot|Claude|Gemini" }
Monitor DNS queries for AI services
Get-WinEvent -LogName "Microsoft-Windows-DNS-Client/Operational" | Where-Object { $_.Message -match "openai|anthropic|cohere" }
- Excessive Permissions: Over-Privileged AI Agents Are a Ticking Time Bomb
AI agents often operate with far more permissions than their work requires. Sonrai computed that 92% of cloud identities are overprivileged, and the proliferation of agents only further exacerbates that problem. To avoid friction, teams often grant agents broad access, and over time, permission creep becomes the norm. A single compromised or malfunctioning agent with excessive permissions can affect multiple resources across an environment before anyone notices.
Step-by-Step Guide to Enforcing Least Privilege:
Step 1: Inventory All AI Identities and Their Permissions
Map all non-human identities and API connections touching your cloud infrastructure. Effective AI security requires knowing which identities exist and what they can access.
Step 2: Apply Least-Privilege Enforcement
Least privilege enforcement for AI agents means actually removing permissions that aren’t actively used, not just flagging them for manual review. Scope creep is especially dangerous in agentic systems because agents act autonomously — an over-privileged agent can make unlabeled changes, trigger deployments, or access sensitive data without human review.
Step 3: Implement Dynamic Permission Models
Traditional static permissions are insufficient for AI agents. Implement dynamic permission models that respond to context and automatically reduce access when not needed.
AWS CLI Command for Auditing Overprivileged IAM Roles:
List all IAM roles and check for overly permissive policies aws iam list-roles --query 'Roles[?contains(AssumeRolePolicyDocument, "Principal")].[RoleName, Arn]' --output table Identify roles with administrative access aws iam list-policies --scope Local --query 'Policies[?contains(PolicyName, <code>Admin</code>)].[PolicyName, Arn]' --output table Generate a credential report to audit unused permissions aws iam generate-credential-report aws iam get-credential-report --query 'Content' --output text | base64 -d
Azure CLI Command for Permission Auditing:
List all Azure AD applications (potential AI identities)
az ad app list --query "[].{DisplayName:displayName, AppId:appId}" --output table
Check service principal permissions
az ad sp list --query "[].{DisplayName:displayName, AppId:appId}" --output table
3. Governance Gaps: The Framework Deficit That’s Widening
The AI governance gap is widening — 61% of organizations lack AI governance technologies. Only 34% of organizations with AI governance audit for misuse, and AI systems deployed without governance are more prone to security incidents and lead to significantly higher breach costs. Frameworks such as NIST AI RMF and ISO 42001 provide structure for governance, while MITRE ATLAS adds adversary-informed insights into real-world threats.
Step-by-Step Guide to Building an AI Governance Program:
Step 1: Adopt a Governance Framework
Choose between NIST AI RMF (flexible, risk-focused) or ISO/IEC 42001 (standards-oriented and prescriptive). Both frameworks share the same foundation: structured, transparent, and risk-informed AI governance.
Step 2: Establish an AI Governance Team
Only 7% of organizations have a dedicated AI governance team. Create a cross-functional team comprising security, legal, compliance, and engineering representatives.
Step 3: Implement Policy and Visibility Controls
Draft an AI Governance Policy defining principles for responsible, ethical, and secure AI use, preferably aligned with ISO 42001. Implement continuous discovery of AI tools and enforce policy at the moment information enters AI interactions rather than attempting to reconstruct exposure after the fact.
Configuration for AI Governance Monitoring (Splunk Query):
Monitor for AI-related API calls in your environment index= sourcetype= "api.openai.com" OR "api.anthropic.com" OR "api.cohere.ai" | stats count by user, src_ip, uri | where count > 10 | table user, src_ip, uri, count
Linux Command for Continuous Compliance Monitoring:
Monitor file access patterns that may indicate unauthorized AI data ingestion auditctl -w /etc/passwd -p wa -k ai_governance auditctl -w /var/log/ -p wa -k ai_governance Check for AI configuration files with excessive permissions find / -1ame "config" -type f -perm -777 2>/dev/null | grep -i "ai|model|llm"
- Data Leakage: Proprietary Information Flowing to External Models
Unchecked shadow AI can lead to proprietary data being ingested by external models and expose intellectual property, increasing the risk of regulatory non-compliance. OWASP’s 2025 guidance reinforces this reality: early adoption without oversight maps directly to LLM02 Sensitive Information Disclosure. Only 11% of organizations feel fully prepared for AI regulation.
Step-by-Step Guide to Preventing AI Data Leakage:
Step 1: Implement Data Loss Prevention (DLP) for AI Interactions
Deploy DLP solutions that monitor and block sensitive data from being sent to external AI APIs.
Step 2: Enforce Data Classification
Classify data based on sensitivity and enforce policies that restrict which data types can be processed by external AI models.
Step 3: Monitor and Block Unauthorized AI API Calls
Use network controls to block or alert on outbound traffic to known AI service endpoints unless explicitly authorized.
Linux iptables Rules to Block External AI APIs:
Block outbound traffic to major AI service providers iptables -A OUTPUT -d 104.18.0.0/16 -j DROP OpenAI iptables -A OUTPUT -d 34.64.0.0/16 -j DROP Anthropic iptables -A OUTPUT -d 34.120.0.0/16 -j DROP Google AI Log all outbound AI API attempts for monitoring iptables -A OUTPUT -d 104.18.0.0/16 -j LOG --log-prefix "AI_API_BLOCKED: "
Windows Firewall Rule to Block AI Services:
Block outbound connections to AI service IP ranges New-1etFirewallRule -DisplayName "Block OpenAI" -Direction Outbound -RemoteAddress "104.18.0.0/16" -Action Block New-1etFirewallRule -DisplayName "Block Anthropic" -Direction Outbound -RemoteAddress "34.64.0.0/16" -Action Block Enable logging for blocked connections Set-1etFirewallProfile -LogBlocked True -LogFileName "%SystemRoot%\System32\LogFiles\Firewall\pfirewall.log"
- Limited Visibility: The Blind Spot That Makes Detection Impossible
Visibility gaps are rampant: 48% of CISOs cited limited visibility into AI usage as a top challenge, making it their second biggest challenge in securing AI systems. Only 9% of organizations monitor AI activity in real time, and while 66% have caught AI over-accessing sensitive data, just 11% can automatically block risky activity. Many compare the security situation around AI to the early days of cloud, when CISOs similarly experienced shadow deployments, unknown risks, and visibility challenges.
Step-by-Step Guide to Gaining AI Visibility:
Step 1: Deploy AI Discovery Tools
Implement tools that continuously discover AI tools and agents across your environment. Only 24.4% of organizations have full visibility into which AI agents are operating in their environments.
Step 2: Implement Real-Time Monitoring
Move from runtime monitoring alone to build-time enforcement, catching risks before agents reach production rather than chasing them afterward.
Step 3: Tag and Classify AI Identities
When an identity meets detection rules, apply durable AI_ACCESS tags so security teams can find and monitor them.
SIEM Query for AI Activity Monitoring (Elasticsearch):
{
"query": {
"bool": {
"should": [
{"match": {"message": "api.openai.com"}},
{"match": {"message": "api.anthropic.com"}},
{"match": {"message": "cohere"}},
{"match": {"message": "huggingface"}},
{"match": {"message": "gemini"}}
],
"minimum_should_match": 1
}
},
"sort": [{"@timestamp": "desc"}],
"size": 100
}
Linux Command for Real-Time AI Traffic Monitoring:
Real-time monitoring of AI-related network traffic sudo tcpdump -i any -1n -A 'port 443' | grep -i "openai|anthropic|cohere|huggingface|gemini" Monitor system logs for AI tool installations sudo journalctl -f | grep -i "ai|llm|model|chatgpt|copilot"
6. Insecure Plugin Design and Supply Chain Vulnerabilities
OWASP identifies insecure plugin design (LLM07) as a critical risk, where compromised registries may distribute skills with inflated permission requests. Over-privileged skills amplify the impact of malicious payloads by providing broader access vectors. The Model Context Protocol (MCP) has emerged as a widely adopted mechanism for connecting LLMs to external tools, but insufficient privilege separation enables privilege escalation, misinformation propagation, and data tampering.
Step-by-Step Guide to Securing AI Plugins and Supply Chain:
Step 1: Vet All AI Plugins and Integrations
Before deploying any AI plugin, review its permission requests and data access requirements. Reject plugins that request more permissions than necessary.
Step 2: Implement MCP Security Controls
For organizations using MCP, implement dynamic permission models and automated trust assessment. Auditing is essential: scope creep happens gradually—for convenience or accidentally through configuration drift—until the agent holds broad or administrative privileges.
Step 3: Monitor for Supply Chain Compromises
Regularly scan AI dependencies for known vulnerabilities and monitor registries for compromised packages.
Dependency Scanning for AI Libraries (Python):
Scan Python AI dependencies for known vulnerabilities
pip-audit
Check for outdated AI packages with security issues
pip list --outdated | grep -i "torch|tensorflow|transformers|langchain|openai"
Use safety for comprehensive vulnerability scanning
safety check --json | jq '.vulnerabilities[] | {package: .package_name, version: .installed_version, advisory: .advisory}'
Dependency Scanning for AI Libraries (Node.js):
Scan npm packages for vulnerabilities
npm audit --json | jq '.advisories[] | {title: .title, severity: .severity, package: .module_name}'
Check for outdated AI-related packages
npm outdated --json | jq '.[] | select(.current | contains("openai") or contains("langchain") or contains("pinecone"))'
What Undercode Say:
- Key Takeaway 1: The six AI security risks identified in enterprise assessments—shadow AI, excessive permissions, governance gaps, data leakage, limited visibility, and insecure plugin design—are not isolated problems. They are interconnected symptoms of the same root cause: AI adoption has outpaced security controls, and organizations are playing catch-up.
-
Key Takeaway 2: The data is sobering: 97% of AI-related breaches lacked proper access controls, shadow AI adds $670,000 to breach costs, and 92% of organizations lack full visibility into their AI identities. These are not edge cases—they are the new normal for organizations that fail to prioritize AI security.
Analysis:
The pattern emerging from thousands of AI security assessments is clear: organizations are treating AI security as an afterthought rather than a foundational requirement. The shift from “can we deploy AI?” to “how do we secure AI?” is happening, but not fast enough. Security teams are drowning in alerts while AI agents operate with administrative privileges across cloud environments.
The governance gap is particularly concerning. With 61% of organizations lacking AI governance technologies and only 7% having dedicated AI governance teams, the regulatory exposure is massive. As regulators worldwide, from the EU to Japan, set expectations through binding legislation, organizations without governance programs face not just security risks but significant compliance liabilities.
The visibility problem compounds every other risk. You cannot secure what you cannot see, and you cannot govern what you cannot inventory. The 92% figure for lack of visibility into AI identities is not just a statistic—it’s a crisis waiting to happen.
The good news is that frameworks exist to guide organizations. NIST AI RMF and ISO 42001 provide structured approaches, while MITRE ATLAS adds adversary-informed insights. The tools and commands provided in this article offer practical starting points for detection and mitigation.
The fundamental lesson is this: AI security is not a technical problem that can be solved with a single tool. It is a governance, visibility, and access control challenge that requires organizational commitment, continuous monitoring, and a proactive rather than reactive posture. Organizations that treat AI security as a strategic priority will avoid the $670,000 breach premium. Those that don’t will become the next headline.
Prediction:
- -1 Organizations that fail to implement AI governance frameworks within the next 12 months will face a 300% increase in AI-related security incidents, driven by regulatory fines and breach costs. The governance gap is widening, and the window for proactive action is closing.
-
-1 The proliferation of autonomous AI agents will outpace identity security controls, leading to a wave of privilege escalation attacks. With 92% of cloud identities already overprivileged, the addition of AI agents will create a perfect storm of excessive permissions and unsupervised actions.
-
+1 Enterprises that adopt NIST AI RMF or ISO 42001 will gain a competitive advantage through regulatory compliance and reduced breach risk. Early adopters of structured AI governance will set the standard for their industries.
-
-1 Shadow AI will become the primary vector for data exfiltration in 2026-2027, as employees continue to deploy unauthorized AI tools without security oversight. The 247-day average detection window will shrink only if organizations deploy continuous discovery tools.
-
+1 The emergence of dynamic permission models and automated trust assessment for MCP agents will revolutionize AI security, enabling real-time least-privilege enforcement. Organizations that invest in these technologies will achieve security at AI speed.
-
-1 The visibility gap will persist, with fewer than 30% of organizations achieving full visibility into AI identities by 2027. This will perpetuate the cycle of unknown risks and reactive security responses.
-
+1 Security teams that shift from runtime monitoring to build-time enforcement will catch risks before agents reach production, fundamentally changing the economics of AI security from detection to prevention.
▶️ Related Video (72% Match):
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