Agentic AI application runtime security and guardrails
Agentic AI apps and chatbots don’t fail like traditional software—they drift, misinterpret, and can be manipulated in real time. They can also be changed by AI teams without security ever being notified. Runtime security and guardrails enforce policies across agentic applications, stopping data leakage, unsafe outputs, and agent misuse before they reach your customers.
Problem
As organizations rush to deploy AI chatbots, copilots, and agents into production, they often expose themselves to hidden risks like data leakage, hallucinations, and policy violations.
Solution
Guardrails adapt to enterprise risk tolerance, allowing both soft detection for visibility and hard blocking for high-severity threats.
“We plugged Defend AI product in with a few lines of code and saw it apply guardrails across prompt injection, toxicity, PII leakage and other agentic threats in under a second, while showing us exactly where it happened. It’s the first solution that lets us push agentic features to production and sleep at night.”
AI you can trust, guardrails you can rely on
80% of Unauthorized
AI Transactions Will Be Due to Internal Policy Violations by 2027 (Gartner)
RankING #1
Prompt Injection top AI Security Risk in 2025 (OWASP)
76.5% OF Enterprise
Lack Confidence in Detecting AI-Powered Attacks (IDC)
The challenge of traditional guardrails for agentic apps
Gen AI creates new attack vectors and risks
The non-deterministic behavior of large language models and natural language interfaces has unlocked new risks—prompt injection, data leakage, hallucinations, and agentic misuse—that upend traditional application security.
Inputs and outputs need context-aware controls
Enterprises need runtime monitoring fine-tuned to their data, policies, and use cases. Harmful behavior can surface through user inputs or model outputs at any point in a session.
AI security and protection needs AI-speed
Blocking unsafe actions requires enforcing governance, preventing data exfiltration, keeping agents on task, and ensuring low-latency, reliable experiences for end users.
Runtime Defense for Safer Agentic Applications
As AI agents gain more power—taking autonomous actions, chaining tools, and even spinning up compute—the risk surface area expands. Runtime guardrails make this complexity manageable, giving enterprises the confidence to scale AI agents, chatbots, and copilots without fear of data leakage, misuse, or policy violations. Guardrails protect customers, preserve trust, and ensure AI delivers accurate, compliant, and reliable results at enterprise speed and scale.
Expert-Driven Safety and Security
A medley of expert models creates a fine-tuned LLM judge that enforces policies and ensures grounded, trustworthy outputs.
Flexible Detection and Blocking
Guardrails adapt to enterprise risk tolerance, allowing both soft detection for visibility and hard blocking for high-severity threats.
Trusted AI at Scale
Deploy copilots, chatbots, and agentic applications with confidence while protecting customers, meeting compliance, and preserving brand trust.
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faq
What are runtime AI guardrails, and why do enterprises need them?
Runtime AI guardrails monitor and control inputs and outputs from agentic AI applications like chatbots, copilots and agents in real time, blocking unsafe or insecure behavior before it reaches users. They’re essential because risks like prompt injection, data leakage, and hallucinations can’t be prevented by traditional AppSec tools.
How are runtime guardrails for AI different from traditional AppSec or RASP?
Traditional AppSec and RASP focus on static code or infrastructure exploits. Runtime AI guardrails are built for non-deterministic systems like LLMs and agents, where vulnerabilities emerge dynamically through language, reasoning, and context.
What types of risks can runtime guardrails prevent in AI agents and chatbots?
Runtime AI guardrails address both non-agentic risks like prompt injection, system prompt leaks, harmful or toxic content, and data exfiltration, as well as agentic risks such as grounding failures, misuse of tools, and excessive autonomy. By covering both layers, enterprises can enforce safe outputs and keep AI agents aligned to their intended tasks in real time.
Can Defend AI’s runtime guardrails be customized to fit my company’s compliance and governance policies?
Yes. Defend AI’s controls can be configured to enforce enterprise-specific policies, risk thresholds, and regulatory requirements, ensuring safe and compliant AI use across different applications.
Do runtime guardrails impact latency or user experience for AI applications?
Straiker’s guardrails are designed for sub-second performance, so AI agents and chatbots stay safe without slowing down users. They deliver security, accuracy, and trust without sacrificing responsiveness.
Protect everywhere AI runs
As enterprises build and deploy agentic AI apps, Straiker provides a closed-loop portfolio designed for AI security from the ground up. Ascend AI delivers continuous red teaming to uncover vulnerabilities before attackers do, while Defend AI enforces runtime guardrails that keep AI agents, chatbots, and applications safe in production. Together, they secure first- and second-party AI applications against evolving threats.
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Resources
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Join the Frontlines of Agentic Security
You’re building at the edge of AI. Visionary teams use Straiker to detect the undetectable—hallucinations, prompt injection, rogue agents—and stop threats before they reach your users and data. With Straiker, you have the confidence to deploy fast and scale safely.
