What is Enterprise AI Security?

Enterprise AI security protects AI systems, models, and data from threats including adversarial attacks, data breaches, model theft, and supply chain vulnerabilities. It extends traditional cybersecurity to address AI-specific attack surfaces.

Why it matters: AI systems process sensitive data, make consequential decisions, and interact with untrusted inputs in ways traditional software doesn't. A single vulnerability can expose customer data, enable fraud, or compromise business-critical systems.

AI-Specific Threat Landscape

Enterprise AI security is part of broader AI governance, AI compliance, and AI risk management programs. For LLM-specific threats, see LLM security.

Prompt Injection Attacks

Attackers craft inputs that manipulate AI behavior—bypassing safety controls, extracting sensitive information, or causing harmful outputs.

Direct injection: Malicious prompts that override system instructions ("Ignore previous instructions and...")

Indirect injection: Poisoning data sources the AI retrieves from—documents, websites, databases—so the AI follows attacker instructions when processing that data.

Jailbreaking: Techniques to bypass safety guardrails and elicit prohibited content.

Data Poisoning

Attackers corrupt training or fine-tuning data to influence model behavior.

• Inject backdoors that activate on specific triggers
• Bias the model toward attacker-desired outputs
• Degrade performance on specific inputs or populations

Model Extraction and Theft

Attackers query the model systematically to reconstruct its behavior or steal intellectual property.

• Model stealing: Replicate a proprietary model through API queries
• Training data extraction: Recover sensitive data memorized during training
• Membership inference: Determine whether specific data was used in training

Adversarial Examples

Carefully crafted inputs that cause models to fail—misclassifying images, misunderstanding text, or producing incorrect outputs. Often imperceptible to humans but highly effective against models.

Supply Chain Attacks

Third-party models, libraries, and APIs introduce vulnerabilities you don't control.

• Compromised foundation models
• Malicious dependencies in ML toolchains
• API providers with security gaps

Data Leakage

AI systems can expose sensitive information in their outputs.

• PII/PHI in generated text
• Proprietary information from training data
• Internal system details revealed through outputs

Security Architecture

Defense in Depth

No single control is sufficient. Layer multiple defenses:

Input layer: Filter, validate, and sanitize inputs before they reach the model. Detect known attack patterns.

Model layer: Use models trained to resist adversarial attacks. Apply differential privacy to limit memorization.

Output layer: Filter outputs for sensitive content. Validate format and content before delivery.

Infrastructure layer: Secure compute, storage, and networking. Apply traditional security controls.

Zero Trust for AI

Don't trust any component implicitly:

• Verify all inputs, including from internal systems
• Validate outputs before acting on them
• Assume third-party models may be compromised
• Monitor all interactions for anomalies

Zero trust requires AI supervision—not just logging what happens, but enforcing constraints that limit what can happen. Supervision ensures that even compromised components can't exceed their boundaries.

Least Privilege

Limit AI system access and capabilities:

• Restrict API access to necessary scopes
• Limit tool/function calling to vetted operations
• Apply rate limits to prevent extraction attacks
• Segment sensitive data access

Security Controls

Prompt Injection Defense

• Input filtering and sanitization
• Instruction hierarchy that prioritizes system prompts
• Output validation against expected patterns
• Adversarial testing and red-teaming

Access Control

• Authentication for all API access
• Role-based permissions for model operations
• Audit logging of all queries and responses
• Rate limiting to prevent extraction attacks

Data Protection

• Encryption at rest and in transit
• Data classification and handling policies
• PII/PHI detection in inputs and outputs
• Retention policies and secure deletion

Model Security

• Version control and integrity verification
• Secure model storage and deployment
• Change management for model updates
• Rollback capabilities for compromised models

Monitoring and Detection

• Real-time anomaly detection on queries
• Output monitoring for sensitive content
• Attack pattern detection and alerting
• Incident response playbooks

Vendor and Third-Party Security

Most enterprises use third-party AI services. Extend security practices to vendors:

Assessment

• Security questionnaires and certifications (SOC 2, ISO 27001)
• Review of AI-specific security practices
• Understanding of data handling and retention
• Incident response capabilities

Contractual Protections

• Data processing agreements
• Security requirements and SLAs
• Breach notification requirements
• Liability and indemnification

Operational Controls

• API security and authentication
• Output monitoring and validation
• Rate limiting and cost controls
• Independent testing of vendor systems

How Swept AI Enhances AI Security

Swept AI provides security controls purpose-built for AI systems:

• Evaluate: Red-team testing that probes for prompt injection vulnerabilities, jailbreak susceptibility, and data leakage risks before production deployment.

• Supervise: Real-time monitoring for attack patterns, anomalous queries, and sensitive content in outputs. Hard policy boundaries that can't be bypassed by clever prompts.

• Security-first architecture: Customer data stays in your environment. No training on your data. Audit trails for all system interactions.

AI security isn't an add-on—it's foundational to deploying AI systems that enterprises can trust.