ENTERPRISE AI
AI Security Architecture
AI systems introduce novel attack surfaces that traditional security architectures were not designed to address. From prompt injection and model extraction to training data poisoning and output manipulation, the threat landscape demands a purpose-built security framework that operates across every layer of the AI stack.
Threat Landscape
AI expands the attack surface in ways most security teams are not yet prepared for.
Traditional cybersecurity focuses on protecting networks, endpoints, and data stores. AI systems introduce entirely new categories of vulnerability. Models can be tricked into producing harmful outputs through carefully crafted prompts. Training data can be poisoned to embed backdoors that activate under specific conditions. Model weights can be extracted through inference APIs, creating intellectual property theft at scale. And AI-generated content can be weaponized for social engineering attacks more sophisticated than anything human adversaries could produce manually.
The challenge is compounded by the speed of deployment. Generative AI tools have been adopted across enterprises faster than any prior technology. Employees are using consumer AI products with enterprise data, often without IT knowledge or approval. Shadow AI is the new shadow IT, and it carries significantly greater risk because the data flows are bidirectional: sensitive information goes in, and AI-generated outputs come out with no audit trail.
Security teams must evolve or risk leaving their organizations exposed to a category of threats they cannot see with current tools. This requires new capabilities, new processes, and a security architecture specifically designed for AI systems.
Defense Architecture
Defense in depth for AI systems
AI security requires layered defenses that protect every level of the stack. No single control is sufficient. The architecture must assume that any individual layer can be breached and ensure that other layers contain the impact.
Perimeter & Access
Data Protection
Model Security
Governance & Audit
Defense in Depth — outer to inner
The defense-in-depth model is not new to security professionals. What is new is the specific controls needed at each layer when AI systems are in scope. The outer layer manages identity and access with zero-trust principles adapted for AI-specific workflows. The data layer protects training data, retrieval corpora, and inference data with encryption, tokenization, and loss prevention. The model layer defends against adversarial attacks, prompt injection, and unauthorized model access. And the governance layer provides the audit trail and compliance monitoring that underpins all of it.
Each layer must be designed not only to prevent attacks but to detect and respond to them when prevention fails. This means real-time monitoring, automated alerting, and incident response playbooks specific to AI security scenarios. A prompt injection attack requires a fundamentally different response than a network intrusion, and security teams must be prepared for both.
Model Security
Protecting the intelligence layer.
Prompt injection defense. Prompt injection is the SQL injection of the AI era. Attackers craft inputs designed to override system instructions, extract system prompts, or cause models to produce outputs they were explicitly designed to avoid. Defense requires input validation, system prompt hardening, output filtering, and adversarial testing as part of the development lifecycle. No single technique eliminates the risk; layered defenses are essential.
Model access controls. Fine-tuned models and proprietary adaptations represent significant intellectual property. Access must be controlled through authentication, rate limiting, and monitoring. Model inference APIs should be treated with the same security posture as the enterprise’s most sensitive data APIs. Without these controls, model extraction through repeated queries becomes a viable attack vector.
Output filtering and guardrails. Even well-designed models can produce outputs that are harmful, inaccurate, or inappropriate for the business context. Output filtering systems act as a final defense layer, catching problematic responses before they reach end users. These should be configurable by use case, with stricter guardrails for customer-facing applications and more permissive settings for internal research tools.
Data Protection
Securing data across the AI lifecycle.
AI systems create data security challenges that extend well beyond traditional data protection. Training data may contain personally identifiable information, proprietary business logic, or copyrighted material. Retrieval-augmented generation systems pull data from enterprise knowledge bases at query time, creating real-time access control challenges. And model outputs can inadvertently leak information about the data they were trained on, a phenomenon known as memorization.
Effective data protection for AI requires a lifecycle approach. Before training: audit data for sensitive content, apply anonymization or synthetic data generation, and establish clear data provenance. During inference: enforce role-based access controls on retrieval systems, encrypt data in transit, and log all data access. After output: filter responses for sensitive information, maintain audit trails, and apply data loss prevention to AI-generated content.
The complexity is significant but manageable with the right architecture. Enterprises that treat AI data protection as an extension of existing data governance, rather than a separate problem, are better positioned to scale securely. The key is adapting existing frameworks rather than building from scratch.
Operational Security
Continuous security for AI in production.
AI security is not a one-time exercise. Production AI systems require continuous security operations: monitoring model behavior for anomalies that may indicate adversarial manipulation, scanning inputs for injection attempts, tracking data lineage to ensure training data integrity, and maintaining real-time visibility into who is accessing what, when, and why.
Incident response for AI systems demands specialized playbooks. When a model produces problematic output, the response must include not just containment and remediation but root cause analysis that considers model behavior, training data, system prompts, and the interaction that triggered the incident. AI incidents often have fundamentally different causal chains than traditional security incidents, and response procedures must reflect this.
Red teaming is essential. Regular adversarial testing of AI systems, conducted by teams with expertise in both traditional security and AI-specific attacks, should be a standard part of the security program. This testing should cover prompt injection, data extraction, jailbreaking, and abuse scenarios specific to the enterprise’s use cases. The findings should feed directly into model hardening, guardrail updates, and security architecture improvements.
Secure your AI systems from the ground up
AI security cannot be an afterthought. We help enterprises design and implement security architectures that protect AI systems across the full stack, from perimeter to governance.