The AI Security Tools Landscape
A map of every major category of tools available to protect AI systems — what each does, what it doesn't do, and when you need it.
You don't need every category immediately. Start with visibility. You cannot govern what you cannot see. Work through the categories in order of priority below.
The Eight Tool Categories
1. AI Gateway / Proxy
What it does: Sits between your applications and AI providers. Every AI request flows through it, giving you centralized visibility, control, and cost management across every model and team.
Key tools: LiteLLM (open source), Azure AI Gateway, AWS Bedrock Gateway, Kong AI Gateway
What it gives you: Unified API key management, per-team usage tracking, cost budgets and alerts, rate limiting, model version control, request/response logging, instant ability to cut off a team or model.
Who needs it: Every organization deploying AI at scale. This is the first tool to deploy.
What it doesn't do: It doesn't evaluate content or make safety judgments — it controls access and provides visibility.
2. Guardrails & Policy Engines
What it does: Evaluates inputs and outputs against defined behavioral rules, enforcing what the AI can and cannot say or do.
Key tools: NeMo Guardrails (NVIDIA, open source), Guardrails AI (open source), Llama Guard (Meta, open source), Lakera Guard (commercial)
What it gives you: Consistent policy enforcement, audit trails of policy decisions, protection against known jailbreak patterns, regulatory compliance demonstration.
Who needs it: Customer-facing AI deployments, regulated industries (finance, healthcare, legal), any AI where specific claims or topics require controlled handling.
What it doesn't do: Guardrails miss novel attacks not in their rules. They are a layer, not a complete solution. They must be actively maintained as new attack patterns emerge.
3. Agent Observability & Tracing
What it does: Records the complete reasoning trace of agent behavior — every thought, every tool call, every intermediate step — so you can understand and audit what agents actually did.
Key tools: LangSmith (LangChain), Langfuse (open source), AgentOps, Arize Phoenix, Helicone
What it gives you: Full audit trail of agent decisions, debugging capability when agents behave unexpectedly, performance monitoring, cost tracking at the agent level, anomaly detection on agent behavior patterns.
Who needs it: Any team running autonomous agents. Essential for incident investigation and compliance demonstration.
What it doesn't do: Observability tells you what happened — it doesn't prevent it. It's your forensic and monitoring layer, not your prevention layer.
4. Secure Execution Environments (Sandboxes)
What it does: Provides isolated, controlled environments for running agent code — separating agent execution from production systems.
Key tools: E2B (cloud sandboxes for agents), Modal (serverless compute), Daytona, Firecracker (AWS, microVMs), standard container isolation (Docker + security profiles)
What it gives you: Code execution without production system exposure, time-limited execution, network-isolated environments, the ability to safely let agents "try" code without risk.
Who needs it: Any agent that can execute code. Non-negotiable for code-executing agents in any environment with access to production data or systems.
5. Red Teaming & Adversarial Testing
What it does: Systematically probes AI systems for vulnerabilities — prompt injections, jailbreaks, data extraction, harmful outputs — before attackers do.
Key tools: PyRIT (Microsoft, open source), Garak (open source), PromptBench, Promptfoo (open source), Rebuff (prompt injection detector)
What it gives you: Discovery of vulnerabilities before production deployment, a repeatable testing process, metrics for AI security posture over time, evidence of due diligence for compliance purposes.
Who needs it: Every team deploying customer-facing or autonomous agents should red team before launch. Security teams should run ongoing red teaming as a continuous practice.
6. Data Security for AI Pipelines
What it does: Detects, redacts, and manages personally identifiable information (PII) and other sensitive data as it flows through AI systems.
Key tools: Private AI (PII redaction), Securiti (AI data governance), Nightfall AI (sensitive data discovery), Microsoft Presidio (open source PII detection)
What it gives you: Automatic PII detection before data reaches AI models, redaction of sensitive information from AI inputs and outputs, audit trails of data handling for GDPR/CCPA compliance, classification of AI-processed data.
Who needs it: Any organization processing customer, employee, or patient data with AI. Increasingly required for regulatory compliance.
7. AI-Specific Threat Detection
What it does: Security monitoring purpose-built for AI/ML systems — detecting model theft, adversarial inputs, data poisoning, and anomalous model behavior.
Key tools: Protect AI (platform), HiddenLayer (ML security), Robust Intelligence (now part of Cisco)
What it gives you: Detection of attacks targeting your AI models specifically, model integrity monitoring, adversarial input detection, integration with existing SIEM/SOC workflows.
Who needs it: Organizations with mature AI security programs and significant AI deployments where model-specific threats are a material risk.
8. Model Evaluation & Safety Testing
What it does: Systematically evaluates AI model capabilities, safety behaviors, and alignment with your requirements — before and during production deployment.
Key tools: OpenAI Evals (open source), Eleuther AI LM Evaluation Harness (open source), Anthropic Evals, BenchLabs, Scale AI evaluation platform
What it gives you: Objective measurement of model behavior on safety-relevant benchmarks, regression testing when models are updated, comparison across model versions and providers, evidence of model suitability for your use case.
Who needs it: Teams selecting models for production use, teams fine-tuning models, and teams whose regulatory environment requires demonstrable model assessment.
Building Your Stack in Priority Order
The AI Security Stack — In the Right Sequence
1 / 5👁️ Start with visibility (Gateway/Proxy)
You cannot govern what you cannot see. Deploy an AI gateway as your first investment. Every request becomes visible, attributable, and controllable. This alone transforms AI from a shadow IT problem into a managed resource.
🛡️ Add behavioral controls (Guardrails)
Once you can see all AI traffic, add guardrails for customer-facing and regulated deployments. Define the rules your AI must follow. Start with the highest-risk use cases — where incorrect AI output has the greatest business consequence.
📊 Instrument agent monitoring (Observability)
As you deploy autonomous agents, add tracing and observability. You need to be able to answer: what did the agent do, why did it do it, and what were the results? This is your forensic capability and your debugging tool.
🔒 Protect data flows (Data Security)
Add PII detection and data classification to your AI pipelines. Know what sensitive data is flowing through your AI systems, where it's going, and whether it's being handled appropriately.
🧪 Test adversarially (Red Teaming)
Before going live with any significant AI deployment, red team it. Use automated tools and human testers to find what your guardrails and controls miss. Make red teaming a recurring practice, not a one-time gate.
The AI Security Maturity Model
- Level 1 — Visibility: AI gateway deployed, all AI usage visible and attributed, cost monitoring in place. You know what you have.
- Level 2 — Controls: Guardrails on customer-facing AI, data security in AI pipelines, sandboxed agent execution, least-privilege permissions documented.
- Level 3 — Testing: Red teaming before all production AI launches, agent observability deployed, anomaly alerting configured, AI incident response playbook exists.
- Level 4 — Program: Continuous red teaming cadence, AI-specific threat detection, regular permission audits, board-level AI security reporting, compliance framework alignment documented.
Most organizations should target Level 2 as their near-term goal. Level 1 is the baseline. Levels 3 and 4 are for organizations with significant AI deployments and mature security programs.