AI Security for Enterprises

AI-Specific Attack Surfaces

AI systems introduce attack surfaces that traditional security tools weren't designed to address. Unlike conventional web applications where inputs are structured and outputs are deterministic, AI systems accept natural language inputs, make autonomous decisions, and interact with external tools — each creating unique security challenges.

Prompt Injection Defense

Prompt injection is the SQL injection of AI systems. Attackers craft inputs that override agent instructions, extract sensitive data, or trigger unintended tool calls. Two variants exist: direct injection (malicious user input) and indirect injection (malicious content in data sources the agent processes).

Credential Management

AI systems touch more credentials than traditional applications — LLM provider API keys, MCP server secrets, database passwords, and service tokens. Distributed agents accessing distributed tools means distributed credential risk. Centralization is the answer.

Access Controls for Agents

Traditional RBAC was designed for humans with static roles. Agents are non-human identities that need tool-specific, method-level, and contextual access controls. A coding agent should read from GitHub but not delete repositories. A QA agent should run tests but not deploy to production.

Beyond tool-level RBAC, method-level restrictions add granularity within each tool. An agent with GitHub access might be allowed to call read_file and create_branch but not delete_repository or force_push. Least privilege is the default — agents only get access to what the current task requires.

Data Protection & DLP

Data flows through AI systems in four directions — each requiring different protection controls. Prompts going to LLMs may contain PII. Responses coming back may leak sensitive data from context. Tool payloads carry database records. Agent-to-agent messages share task context.

DLP controls operate at the gateway level: PII scanning detects and redacts SSNs, credit card numbers, emails, and phone numbers before they reach LLM providers. Secret scanning catches API keys, passwords, and certificates. Data classification tags content by sensitivity level, enabling policy enforcement per classification.

Network Security

AI infrastructure requires the same network security fundamentals as any enterprise system — plus additional considerations for agent-to-provider communication and multi-agent messaging.

Threat Detection & Response

AI-specific threat indicators look different from traditional security events. Watch for unusual tool call patterns, cost anomalies, PII detection spikes, authentication failures, and unknown agent identities connecting to your infrastructure.

Zero-trust security by design

Learn more

Axiom's Security Architecture

Axiom's AI Gateway implements security at every layer of the stack — from network-level mTLS to application-level PII scanning. Security isn't a feature that gets added later; it's the foundation that everything else builds on.

Compliance support includes SOC 2 Type II audit trails, HIPAA-compatible data handling with BAA support, and ISO 27001-aligned security controls. Every security event is exportable to your SIEM in structured JSON format for centralized threat monitoring.