SECURITY GUIDE • 2026 EDITION

Enterprise AI Security
& Governance Guide

The definitive playbook for securing AI/ML systems in production. From threat modeling to compliance frameworks, everything enterprises need to protect their AI investments.

340%
Increase in AI attacks (2025)
$4.2M
Avg. AI breach cost
67%
Lack AI security policy

⚡ QUICK ANSWER: What is AI Security?

AI Security encompasses the practices, tools, and frameworks used to protect artificial intelligence systems from attacks, misuse, and unintended behavior. For enterprises in 2026, this includes: (1) protecting AI models from adversarial attacks, data poisoning, and prompt injection, (2) securing the ML pipeline from training to deployment, (3) implementing governance frameworks for responsible AI use, (4) ensuring compliance with emerging AI regulations (EU AI Act, local laws), and (5) monitoring AI systems for drift, bias, and anomalous behavior. Organizations deploying AI without security measures face an average of 3.2x higher risk of data breaches.

01 // AI SECURITY THREATS

Understanding the AI Threat Landscape

AI systems face unique attack vectors that traditional security measures cannot address.

💉

Prompt Injection

Malicious inputs designed to override AI instructions, extract training data, or cause unintended actions. The #1 threat to LLM applications.

CRITICAL
🧪

Data Poisoning

Corrupting training data to introduce backdoors or bias into models. Can occur during initial training or continuous learning.

HIGH
🎭

Model Extraction

Attackers query your API to reverse-engineer proprietary models, stealing your IP and competitive advantage.

HIGH
👁️

Model Inversion

Extracting sensitive training data from model outputs. Can expose PII, trade secrets, or confidential information.

HIGH
🎯

Adversarial Examples

Carefully crafted inputs that cause misclassification. Critical for safety systems, fraud detection, and autonomous vehicles.

MEDIUM
📉

Model Drift & Decay

Performance degradation over time as data distributions change. Can lead to biased or incorrect decisions without monitoring.

MEDIUM

The Attack Surface is Expanding

Every component of your AI stack presents potential vulnerabilities:

  • Data layer: Training data, feature stores, data pipelines
  • Model layer: Model weights, architecture, hyperparameters
  • Infrastructure layer: GPUs, cloud services, containers
  • Application layer: APIs, user interfaces, integrations
  • Human layer: Data scientists, ML engineers, business users
02 // GOVERNANCE FRAMEWORKS

AI Governance Frameworks

Establishing policies and processes for responsible AI deployment.

📋

NIST AI RMF

The US National Institute of Standards framework for managing AI risks. Covers governance, mapping, measuring, and managing AI risks.

🇪🇺

EU AI Act

The world's first comprehensive AI law. Risk-based approach with strict requirements for high-risk AI systems.

🔐

ISO/IEC 42001

International standard for AI Management Systems. Provides certification path for organizations deploying AI.

🏛️

Singapore AI Governance

Model AI Governance Framework from IMDA. Practical guidance for organizations in APAC deploying AI.

Building Your AI Governance Structure

Effective AI governance requires clear roles and responsibilities:

  • AI Ethics Board: Cross-functional team overseeing AI strategy and ethical considerations
  • AI Security Officer: Dedicated role for AI-specific security concerns
  • Model Risk Management: Ongoing assessment and monitoring of AI risks
  • Data Governance: Policies for training data quality, privacy, and lineage
  • Incident Response: AI-specific playbooks for security incidents
03 // LLM & GENAI SECURITY

Securing Large Language Models

Special considerations for generative AI and LLM deployments.

The OWASP Top 10 for LLMs

The Open Web Application Security Project has identified the top security risks for LLM applications:

  1. LLM01: Prompt Injection - Crafted inputs that hijack model behavior
  2. LLM02: Insecure Output Handling - Trusting LLM outputs without validation
  3. LLM03: Training Data Poisoning - Corrupted data affecting model behavior
  4. LLM04: Model Denial of Service - Resource exhaustion attacks
  5. LLM05: Supply Chain Vulnerabilities - Compromised models or dependencies
  6. LLM06: Sensitive Information Disclosure - Leaking training data or PII
  7. LLM07: Insecure Plugin Design - Vulnerable integrations and tools
  8. LLM08: Excessive Agency - Too much autonomy without guardrails
  9. LLM09: Overreliance - Trusting AI without human oversight
  10. LLM10: Model Theft - Extracting proprietary model capabilities

🛡️ LLM Security Controls

✓
Input Validation & Sanitization Filter and validate all user inputs before sending to the LLM. Block known injection patterns.
✓
Output Filtering Scan LLM outputs for sensitive data, PII, and malicious content before returning to users.
✓
Rate Limiting & Quotas Prevent abuse and DoS attacks with per-user and per-IP rate limits on API calls.
✓
Prompt Hardening Use system prompts with clear boundaries. Implement prompt defense techniques like XML tagging.
✓
Human-in-the-Loop Require human approval for high-stakes actions. Never give LLMs direct access to critical systems.
04 // SECURE MLOPS

Security in the ML Pipeline

Integrating security throughout the machine learning lifecycle.

Pipeline Stage Security Measures Tools
Data Collection Data validation, PII detection, consent management Great Expectations, Presidio
Data Storage Encryption at rest, access controls, audit logging AWS S3 + KMS, Azure Blob + Key Vault
Training Secure compute, differential privacy, federated learning TensorFlow Privacy, PySyft
Model Registry Model signing, versioning, vulnerability scanning MLflow, Weights & Biases
Deployment Container security, secrets management, network isolation Trivy, HashiCorp Vault, Istio
Monitoring Drift detection, anomaly detection, bias monitoring Evidently AI, Fiddler, Arthur
05 // REGULATORY COMPLIANCE

Navigating AI Regulations

Understanding the evolving landscape of AI-specific laws and requirements.

Regulation Scope Key Requirements Penalties
EU AI Act All AI systems in EU market Risk classification, conformity assessment, transparency Up to €35M or 7% revenue
US Executive Order on AI Federal agencies, critical infrastructure Safety testing, red teaming, watermarking Varies by sector
Singapore PDPA + AI AI processing personal data Consent, purpose limitation, data protection Up to S$1M
Vietnam Cybersecurity Law AI with Vietnamese user data Data localization, security assessments Varies
06 // SECURITY CHECKLIST

AI Security Implementation Checklist

A practical checklist for securing your AI deployments.

🔒 Essential Security Controls

✓
AI Asset Inventory Document all AI models, their purpose, data sources, and risk level
✓
Threat Modeling Conduct AI-specific threat modeling for each deployment
✓
Access Controls Implement least-privilege access for models, data, and infrastructure
✓
Model Monitoring Deploy continuous monitoring for drift, bias, and anomalies
✓
Incident Response Plan Create AI-specific playbooks for security incidents
✓
Red Team Testing Regularly test AI systems with adversarial attacks
✓
Data Lineage Track training data provenance and maintain audit trails
✓
Employee Training Train all AI users on security risks and responsible use

Secure Your AI Investment

Our AI security experts can help you implement governance frameworks, conduct risk assessments, and secure your AI deployments.

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