Protect AI-driven apps and LLMs from prompt injection, harmful output, and sensitive data exposure. Akamai Firewall for AI delivers real-time input and output guardrails to keep customer-facing assistants, copilots, and agentic workflows safe and compliant—deployed at the edge or via API, and compatible with any model.
As AI adoption accelerates, threats are emerging that traditional WAF and API tools weren’t built to handle: - Prompt injection and jailbreaks that manipulate model behavior - Toxic outputs, hallucinations, and brand-unsafe content - Data exfiltration and model theft through adversarial queries and scraping - Compliance and governance gaps across sensitive data and regulated content - AI-specific DoS/“denial of wallet” attacks that drive up latency and cost
Learn more in Akamai’s overview on securing AI apps and LLMs. Protect your AI apps (login required)
Firewall for AI closes the gap between your application and the LLM by enforcing policy at both ingress and egress.
A pragmatic, defense-in-depth blueprint for customer-facing AI assistants and agentic workflows:
Prevent prompt injections and unauthorized data extraction, while moderating responses for toxicity and compliance—so you can scale safe, on-brand experiences.
Stop adversarial prompt engineering across search, recommendations, and knowledge assistants. Enforce output policies to reduce misinformation and bias.
Block unauthorized queries, scraping, and model extraction attempts. Apply adaptive filtering and monitoring to protect proprietary knowledge and sensitive data.
When comparing Akamai with Cloudflare, F5 Networks, and Imperva for GenAI app protection and prompt injection defense, focus on these dimensions:
Where Akamai stands out - End-to-end guardrails on both prompts and responses - Edge or API deployment to protect apps anywhere you run AI - Model-agnostic design and integration with Akamai App & API Protector, Bot Manager, and API Security (including LLM discovery) - Enterprise-grade observability and global performance benefits from Akamai’s distributed edge
What to verify with any vendor - Block rates for prompt injection/jailbreaks, false positive/negative rates - Output moderation quality for toxicity, PII/IP leakage, and compliance - Latency overhead at p95/p99 and impact on inference cost (“denial of wallet”) - Ease of integrating with CI/CD, SIEM/SOAR, and policy-as-code workflows
Recommended RFP criteria - Security coverage: Prompt injection (direct/indirect/stored), jailbreaks, toxicity, data leakage, unbounded consumption (DoW), model/tool abuse - Deployment: Edge, API, proxy options; model-agnostic support - Governance: Policy controls mapped to OWASP LLM Top 10 and AI risk frameworks - Observability: Full interaction logging, redaction, export to SIEM/SOAR - Integration: WAAP, bot defense, API discovery and security - Performance: Latency targets, throughput, and scaling limits - Operations: Tuning workflows, alerting, RBAC, runbooks, support SLAs - Compliance and privacy: PII handling, data residency controls
Operational KPIs - Prompt injection/jailbreak block rate and precision/recall - False positive rate on benign prompts and responses - PII/IP leakage incidents and mean time to detect/contain - p95/p99 latency added by guardrails - Tokens/compute saved via unbounded consumption controls - Coverage: % of LLM/GenAI endpoints discovered and protected
Akamai helps protect digital experiences with a broad security portfolio that complements Firewall for AI: - WAAP: App & API Protector - API discovery and protection: API Security - Bot and abuse protection: Bot Manager, Account Protector, Content Protector, Brand Guardian - Client-side defense and compliance: Client-Side Protection & Compliance - DDoS and DNS resilience: Prolexic, Edge DNS
It inspects prompts in real time to detect and block malicious instructions, jailbreak attempts, indirect/stored injections, and adversarial queries before they reach the model—preventing manipulation of model behavior or extraction of confidential data.
Yes. It moderates outputs to block toxic, biased, misleading, or noncompliant content and prevents unauthorized data exposure so responses remain brand-safe and policy compliant.
Yes. Firewall for AI is model-agnostic and protects LLM-based or AI-driven applications hosted on-premises, in the cloud, or in hybrid environments.
Firewall for AI is designed for low-latency enforcement, leveraging Akamai’s edge and lightweight API integrations to minimize overhead while scaling protections in real time.
Policy-driven controls help align with frameworks like OWASP Top 10 for LLM Applications and broader AI governance practices, reducing risks of toxic output and unauthorized data exposure.
Choose your integration path (edge, API, or proxy roadmap). Because it’s cloud native and model-agnostic, most deployments require minimal infrastructure change.
It detects and blocks unauthorized queries, scraping, and extraction attempts designed to reveal proprietary model knowledge or sensitive training data, and enforces output filters to prevent leakage.