Your AI agent just executed a database query. You don’t know what it is. You don’t know why it ran. You see it in the logs three hours later, after the damage is done.
That’s the state of AI agent security today.
Agents are powerful. They write to production databases. They call APIs. They move files. They trigger workflows. Unlike traditional applications, which are deterministic systems where every code path gets reviewed before deployment and behavior is predictable, agents behave in non-deterministic and evolving ways at runtime. They make decisions. They execute. We react.
EXECUTIVE SUMMARY
AI runtime control introduces a real-time enforcement layer that intercepts every AI agent action before execution, enabling proactive control and continuous visibility over AI agent actions by evaluating risk across multiple dimensions and enforcing decisions within milliseconds. Unlike observability and governance tools that operate after actions occur, runtime control prevents malicious behavior before it happens. AI runtime security also provides operational evidence needed to support governance, oversight, and accountability for live AI behavior, as traditional documentation is insufficient. This article explains the full interception pipeline, from agent decision to enforcement, and shows how organizations can secure AI systems at execution time.
The gap between what an agent decides to do and what actually happens is where attacks live. Prompt injection. Goal hijacking. Data exfiltration. Privilege escalation. Traditional security measures assume predictable behavior, but this assumption fails for AI agents whose actions can change dynamically during runtime. By the time you see what happened, it’s too late.
Runtime control closes that gap. Achieving runtime visibility is essential to manage risk, as the most consequential AI risks only appear after deployment, particularly when agents interact with real users, data, and systems.
The Problem With “See It Later” Security for AI Agents
Observability tells you what happened. Monitoring tells you what went wrong. Post-incident analysis tells you how it happened. None of that stops the thing from happening in the first place.
An attacker jailbreaks your agent. The agent decides to exfiltrate customer PII. It calls your API to dump the data. By the time your security team wakes up to an alert, thousands of records are gone. Security teams can deploy runtime controls to detect abuse and prevent sensitive data exposure, proactively stopping threats before they escalate.
Runtime control doesn’t wait for the alert. It intercepts the decision and enforces a policy before the action executes. The system scans model outputs in real time to prevent the exposure of confidential data. Same attacker, same jailbreak, same API call, but you reject it at the boundary. The agent never touches the data.
This is fundamentally different from observability. You’re not looking at logs. You’re making enforcement decisions in the execution pipeline, at millisecond scale. Logging systems are still essential to provide visibility into AI activities and support audit and compliance functions as part of a comprehensive security approach.
How Runtime Control Works: The Interception Pipeline
AI runtime control is essential for securing actively operating AI systems, as it ensures that core capabilities such as enforcement mechanisms and runtime protection are in place to manage and regulate system behavior in real time. Here’s what happens, step by step, when an agent tries to execute an action.
Effective AI application protection requires continuous monitoring and real-time enforcement of policies to prevent unauthorized actions and ensure compliance with governance frameworks.
Step 1: The Agent Makes a Decision (0ms)
Your agent processes a request—often initiated by user input or external triggers. Agent frameworks, such as LangChain or custom solutions, manage these decision points. The agent may call a function, query a database, or attempt to write a file. This is the trigger point.
Traditional workflow: The action executes. You log it. You move on.
Runtime control workflow: The function call gets intercepted before it runs.
Step 2: Policy Engine Receives the Request (1-2ms)
The Vaikora SDK wraps your tool calls. When an agent tries to invoke a function, the wrapper intercepts it and sends the request to the policy engine.
The policy engine receives:
The action (e.g., “delete records from customer_profiles”)
The target resource (e.g., “customer_profiles table in production”)
The agent identity (e.g., “data_analysis_agent_v2”)
The user context (who triggered the workflow)
The execution environment (production vs. staging)
The request timestamp
Any data payloads involved
All of this information gets packaged and sent to the policy decision point. Latency so far: 1-2 milliseconds.
Step 3: Risk Scoring (3-8ms)
The policy engine evaluates the action across seven risk dimensions, each weighted based on how much they matter for your specific risk profile. Risk teams rely on continuous monitoring of these dimensions to manage risk in real time.
Action Risk (1.5x weight): What is the agent actually trying to do?
Reading customer data: lower risk
Modifying records: medium risk
Deleting records at scale: higher risk
Exfiltrating data to an external API: critical risk
High-risk actions, such as exfiltrating data or deleting records at scale, trigger stricter enforcement measures to reduce risk, including inline controls and active blocking.
The system assigns a base risk score for the action type. Deletion operations start higher than reads. External API calls start higher than internal operations.
Agent Risk (1.2x weight): Which agent is making this request?
What’s its history? Has this agent triggered security alerts before?
What permissions does it have? Is it running with elevated credentials?
What’s its normal behavior baseline? Has it deviated significantly?
High risk agents—such as those with a history of anomalies, elevated permissions, or significant behavioral deviations—are subject to stricter policies and may be actively blocked, while lower-risk agents may only trigger alerts.
A well-behaved agent with a clean history gets a lower agent risk score. A newly deployed agent or one with a history of anomalies gets higher weight.
Temporal Patterns (1.0x weight): What time is it?
Is this action happening during normal operating hours?
Is this agent supposed to run at 3 AM?
Database modifications at midnight from an agent that normally runs at 9 AM signal something wrong.
The system maintains baselines for when each agent typically operates.
Environmental Context (0.8x weight): Where is the agent running?
Production or staging? (Production gets higher scrutiny)
On a trusted internal network or over the internet?
In an approved data center or an unfamiliar cloud region?
Environmental anomalies lower trust but carry less weight than action risk.
Behavioral Deviations (1.3x weight): Is this action consistent with the agent’s normal behavior?
This agent normally reads customer profiles
Now it’s trying to dump employee salary data
This is a significant deviation and gets flagged heavily
The system learns baseline behavior patterns for each agent over time. Large deviations from learned baselines increase risk substantially.
Compliance Requirements (1.1x weight): Does this action violate regulations?
PCI DSS rules about payment data access
HIPAA rules about health data movement
SOX rules about financial data modification
GDPR rules about PII handling
Compliance violations carry explicit weight in the risk calculation.
Data Sensitivity (1.0x weight): What data is involved?
Test data: low sensitivity
Production customer email addresses: medium sensitivity
Production customer SSNs, credit cards, health records: high sensitivity
The system classifies data by sensitivity level and factors that into the score.
These seven dimensions combine into a composite risk score from 0 to 100. The calculation happens in parallel across all dimensions to keep latency low.
Latency for complete risk scoring: 3-8 milliseconds.
Effective risk management in AI-driven environments requires continuous monitoring to track inputs, outputs, and behaviors in real time, rather than relying solely on reactive visibility tools.
Step 4: Parallel Threat Detection and Prompt Injection Attacks (10-45ms)
While the policy engine scores the action, twelve threat detection vectors run in parallel:
Prompt injection detection: Does the request look like a jailbreak attempt? Direct injection or indirect?
SQL injection patterns: Are there SQL injection signatures in query strings?
Cross-site scripting (XSS): Are there XSS payloads in data fields?
Data exfiltration velocity: Is the agent trying to move unusually large amounts of data?
PII detection: Are there regex patterns matching SSNs, credit cards, health IDs?
Known exploit signatures: Does the request match known attack patterns?
Resource exhaustion patterns: Is the agent trying to query with parameters that would lock the system?
Privilege escalation attempts: Is the agent requesting permissions it doesn’t have?
Session anomalies: Does the session context match the agent’s identity?
Rate limiting violations: Has this agent made too many requests too quickly?
Policy violations: Does the action violate existing data access policies?
Behavioral anomalies: Does the action deviate from the agent’s learned behavior baseline?
Each vector runs independently. If any vector detects a threat signature, it flags the request.
Latency for the 99th percentile: under 50 milliseconds for the complete detection pipeline.
Step 5: Policy Decision (1-2ms)
The policy engine combines the risk score and threat detection results, then enforces security policies and controls in real time by applying your policies to make a decision. This includes enforcing controls such as continuous inspection and dynamic policy enforcement during model decision-making.
Policies are granular. You can have different rules for different agents, different data resources, different times of day:
If risk score > 85 and threat vector matches “PII exfiltration”: Block and alert security team (automated responses are triggered for certain policy violations)
If risk score 60-85: Require approval from a human reviewer
If risk score 40-60: Sandbox the action (let it run but isolate writes)
If risk score < 40: Allow the action
Real-time governance enforces guardrails such as blocking unauthorized email actions or database writes without approval.
You can layer multiple policies. You can have exceptions. You can set different thresholds for production vs. staging.
Step 6: Enforcement (1-5ms)
The policy engine enforces the decision by actively monitoring both runtime behavior and agent behavior to ensure compliance with security policies.
If the decision is Allow, the original function call executes normally.
If the decision is Block, the agent gets back a safe error response. It doesn’t know why. It gets fallback logic and continues.
If the decision is Require approval, the action pauses. A human (SOC analyst, security team lead, whoever owns the approval queue) gets a notification. They review the context. They make a call. The agent waits, then continues or fails based on the approval.
If the decision is Sandbox, the action executes but in an isolated environment. The agent gets back results that look normal. But the actual write operations don’t commit to production. Useful for testing whether an agent has been compromised.
Runtime enforcement is especially important for autonomous agents and agent driven workflows, as these autonomous AI agents operate independently and interact dynamically with systems and data. Runtime systems also ensure that if an AI agent crashes mid-task, the state is persisted and the task can resume.
Step 7: Logging and Audit (1-2ms)
Every decision gets logged. The request, the risk score, the threat detection results, the policy applied, the decision, the enforcement action—all are recorded in logging systems that provide runtime visibility into AI operations. These logs, secured with a SHA-256 hash chain to ensure immutability, support audit and compliance functions by tracking every input and output. You cannot modify a log entry without breaking the chain.
For compliance purposes, you have forensic history: the exact sequence of events, the exact risk score, the exact policy rule, and the exact enforcement decision, all available for thorough investigation and audit.
Total Latency: From Decision to Enforcement
Median: 8-15 milliseconds
95th percentile: 20-30 milliseconds
99th percentile: under 50 milliseconds
This is fast enough to be part of the execution pipeline, not separate from it. The agent submits a request. The policy engine evaluates it. You enforce a decision. The agent gets a response back. All before the agent would normally move to the next step.
Different From Observability
Observability tools log what happened. They tell you after the fact that something went wrong. You write a postmortem. You rotate a secret. You hope it doesn’t happen again.
Runtime control prevents the thing from happening in the first place by acting as a security layer during live AI operations.
Observability is important. You need visibility into what your agents are doing, but continuous visibility is a key feature of runtime security, enabling ongoing monitoring and oversight to detect and prevent potential threats. However, observability without enforcement is incident response, not security.
Different From Model Security
Model security tries to prevent the LLM itself from having bad thoughts—guardrails around the model, prompt injections that are caught before they reach the LLM.
Runtime control works on the actions that result from those thoughts, whatever they are. It is a core component of AI security, specifically designed to address runtime threats that emerge as AI applications operate dynamically in real-world environments.
Some agents will be compromised. Some will be jailbroken. Some will have been poisoned by an attacker. Runtime control doesn’t care about preventing the thought. It intercepts the action. Monitoring AI behavior in real time is essential for preventing misuse and ensuring responsible operation.
An attacker succeeds in jailbreaking your agent. The agent decides to exfiltrate data. Runtime control sees that action and blocks it. The jailbreak happened. The action didn’t.
Different From Governance Tools
Governance tools enforce policies at the data access layer. They say which users can access which data. They’re about authorization: does this principal have permission to access this resource.
Runtime control is about behavior: does this action, from this agent, in this context, at this time, with this data, match the security policy. Runtime enforcement is essential in production environments, where AI use is dynamic and ongoing, to ensure that AI systems operate securely and in compliance with organizational policies.
An agent might have permission to read customer profiles. But reading one customer profile is normal. Reading 100,000 customer profiles at 3 AM and exfiltrating them to an external API is abnormal. Governance would allow it. Runtime control would block it.
Framework Compatibility
Vaikora works with LangChain, AutoGen, CrewAI, and custom agent implementations, and is compatible with a wide range of ai environments and agent frameworks. The API is OpenAI-compatible, so you change one URL and add an auth header. No SDK migration required for existing OpenAI, Anthropic, or Azure client code. SDKs are available for Python (sync + async), Node.js/TypeScript (with Express middleware), and React/Next.js (with SecureInput component and SecurityBadge widget).
Supports 12 LLM providers: OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, Together AI, Groq, Ollama, and custom vLLM. Provider fallback routing is built in, with automatic failover using latency, cost, or round-robin strategy.
Operational Controls IT Directors Care About
A few things that matter in production that most marketing pages skip:
Fail-open vs. fail-closed. When the Vaikora gateway times out (default 5 seconds), you choose whether traffic passes through unprotected (fail-open) or gets dropped (fail-closed). This is an availability vs. security tradeoff you need to decide explicitly for your environment.
Content-free logging. Set content: false in config and Vaikora stores only metadata, never the actual prompt or response content. Only request IDs, risk scores, decisions, and timestamps get logged. This is the difference between passing and failing a HIPAA audit.
Budget controls. Monthly, daily, and per-API-key spend limits with configurable alert thresholds. When a limit is hit, Vaikora can throttle or block automatically. Prevents runaway agent costs from a compromised key.
Built-in red teaming. vaikora redteam –suite comprehensive generates an HTML report testing your policies against adversarial attacks. You can also target specific vectors: vaikora test –attack-type prompt_injection –variations 100. This ships with the product, not as a separate service.
Topic restriction. Configure an allow-list of on-topic subjects. Off-topic requests get redirected with a custom message. Keeps customer-facing agents from wandering into areas they shouldn’t discuss.
Reversible PII redaction. Vaikora replaces real personally identifiable information (PII) such as SSNs, credit cards, emails, and medical records with synthetic data that looks realistic before the LLM sees it. This ensures that sensitive data and sensitive information are protected, and the LLM processes coherent context without exposure to real data. On the way back, Vaikora reverse-maps the synthetic tokens to the originals. Three redaction modes: synthetic (reversible fake data), mask, or hash. With preserve_format: true, a redacted credit card still looks like a credit card number. The LLM never touches real PII or other sensitive information. This is architecturally stronger than simple masking because the model still gets usable context. Runtime controls like these also help prevent data leakage during live interactions, ensuring sensitive data and PII are not inadvertently exposed.
The rapid adoption of AI systems increases the risk of shadow AI—undeclared, unmonitored AI workloads that can operate without oversight and expose organizations to vulnerabilities. This makes robust governance frameworks and runtime controls essential to manage these challenges effectively. Additionally, AI driven optimization can cut data center energy consumption by up to 30% through continuous micro-adjustments to match actual demand.
The Real Value
You’ve deployed an AI agent into production. It’s touching real systems. Real data. Real workflows.
The moment it got power is the moment you needed a control plane between its decisions and their execution.
AI runtime security and runtime security are essential for protecting AI applications and the underlying AI model during live operation. Runtime control is that layer. It’s not a firewall. It’s not an IDS. It’s a security decision engine that runs fast enough to be embedded in the execution pipeline.
Monitoring runtime behavior is critical for maintaining security and compliance, as it allows for real-time observation and enforcement based on the actual actions of AI systems.
Every agent action gets scored. Every threat gets detected. Every decision gets enforced. Every decision gets logged immutably.
That’s runtime control.
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Vaikora gives security teams real-time enforcement, behavioral analytics, and immutable audit logs for every AI action in your environment.