AI Runtime Security is the Security Layer AI Can’t Outgrow

What is AI Runtime Security?

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AI runtime security focuses on protecting AI applications and agents while they are actively operating. It covers execution-time behaviors like input processing, reasoning, invoking tools, accessing data, and executing actions.

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Unlike traditional application security, which secures code paths and inputs before deployment, runtime security assumes that risk emerges dynamically. At runtime, GenAI systems are non-deterministic, stateful, and often agentic. They adapt their behavior based on context, memory, intermediate reasoning steps, and external integrations.

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In practical terms, AI runtime security is about controlling:

• What an AI model is allowed to do
• Under which identity and permissions
• In response to which inputs
• Within which boundaries of intent

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This becomes especially critical for agentic systems, where models don’t just generate outputs but plan, decide, and act.

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Why AI Runtime Security from Runtime Controls Is Critical

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Pre-deployment testing, prompt hardening, and static guardrails are necessary, but no longer sufficient. The most consequential AI risks only appear after deployment, when models interact with real users, real data, and real systems. These risks are especially acute when agentic AI is in play.

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Production-Only Threat Exposure

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Many of the most dangerous agentic vulnerabilities (such as agent goal hijacking or rogue agents) cannot be reliably detected in staging or red-team environments.

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They emerge only when real permissions are in play. Runtime security assumes compromise will occur in production, and focuses on limiting blast radius.

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Real-Time Model Abuse and Misuse Risks

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At runtime, attackers don’t need to “break in.” They can influence behavior.

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Examples include:

• Manipulating an agent’s goals or planning steps
• Forcing misuse of legitimate tools and APIs 
• Abusing inherited identities and privileges 
• Triggering unexpected code execution through natural language 

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These are not input validation problems. They are execution control problems, and they require real-time oversight of what the AI is doing, not just what it is saying.

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Gaps Left by Static and Pre-Release Controls

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Static controls assume predictable behavior. GenAI systems are anything but.

Traditional approaches struggle with:

• Memory and context poisoning that persists across sessions
• Cascading failures where small errors amplify across multi-step workflows
• Inter-agent communication risks that bypass single-agent guardrails

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Once deployed, models evolve through interaction, even if the underlying weights never change. Without runtime enforcement, post-deployment drift becomes invisible.

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Compliance and Audit Readiness for Live AI Systems

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Regulators and auditors increasingly expect answers to questions like:

• Who authorized this action?
• Why was this output generated?
• Which data sources influenced this decision?
• What controls were enforced at the moment of execution?

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Static documentation can’t answer those questions alone.

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Runtime security provides:

• Continuous visibility into agent actions and decisions
• Enforceable policies tied to live context
• Audit trails that reflect actual behavior, not intended design

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In other words: compliance for AI is no longer a design-time exercise. It’s a runtime discipline.

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AI Runtime Threats

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At runtime, GenAI applications stop being static models and start behaving like live actors inside your environment. They interpret instructions, call tools, retrieve data, and even decide what to do next.

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That’s what makes runtime threats fundamentally different. The risk isn’t just what data goes in or comes out, but how the AI reasons, acts, and adapts in real time, often with legitimate access and no obvious signs of compromise.

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Below are the core AI runtime threats enterprises need to understand, especially as GenAI systems gain autonomy.

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How AI Runtime Security from Modern Platforms Works

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Modern AI runtime security platforms operate alongside GenAI applications and agents, observing and enforcing controls as execution happens. Rather than relying on static analysis or pre-deployment testing, they focus on live telemetry, behavioral analysis, and real-time policy enforcement across production environments.

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At a high level, runtime security is built around continuous observation, contextual decision-making, and automated response.

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Runtime Telemetry Collection Across AI Applications

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The foundation of runtime security is telemetry. Modern platforms collect execution-level signals across AI applications, including prompts, retrieved context, tool calls, identity context, and resulting actions.

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This telemetry is gathered consistently across different models, applications, and agent frameworks, creating a unified view of how AI behaves in production. Without this layer, security and risk teams are effectively blind to what AI systems are actually doing.

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Input and Output Inspection at Inference Time

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Runtime platforms inspect both inputs and outputs at inference time, when risk is introduced and decisions are made.

This includes:

• User prompts and indirect inputs from RAG or external systems
• Intermediate reasoning or planning artifacts, where available
• Generated outputs before they are returned or acted upon

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Inspecting data at this stage allows platforms to detect prompt manipulation, sensitive data exposure, and policy violations before impact occurs.

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Detection of Risky or Unexpected Runtime Behavior

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Beyond single interactions, runtime security platforms analyze behavioral patterns over time. This enables detection of subtle risks such as goal drift, abnormal tool usage, or escalating access patterns.

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By correlating runtime behavior across sessions and workflows, platforms can identify deviations from expected behavior that static testing would miss—especially in long-running or agentic systems.

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Policy Enforcement and Guardrails

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Runtime policies define what AI applications and agents are allowed to do, under which conditions, and with which resources.

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These guardrails are enforced dynamically based on:

• Identity and permission context
• Sensitivity of accessed data
• Type of tool or action being invoked
• Current risk level or anomaly score

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Unlike hard-coded controls, runtime policies can adapt to context, enabling enforcement without blocking legitimate use cases.

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Automated Risk Response and Mitigation

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When risky behavior is detected, modern platforms support automated responses to reduce impact and contain risk.

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These responses may include:

• Blocking or modifying outputs
• Restricting tool access or permissions
• Triggering additional verification or human review
• Logging and alerting for investigation

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Automation is critical at runtime, where decisions and actions happen faster than manual intervention can realistically keep up.

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AI Runtime Security from an Architecture and Deployment View

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From an architectural standpoint, AI runtime security is defined by where controls sit in the execution path, what layers they can observe and enforce, and how cleanly they integrate into existing deployment models. These decisions determine not only security coverage, but also latency, reliability, and operational complexity.

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Understanding these tradeoffs is critical when securing production GenAI applications and agentic workflows.

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Inline Versus Out-of-Band Runtime Enforcement

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Runtime enforcement can be deployed either inline or out-of-band, each with distinct implications.

• Inline enforcement places controls directly in the request/response path between users, applications, models, and tools. This enables deterministic prevention, blocking, modifying, or gating actions before they execute. But it introduces strict requirements around latency, availability, and failure handling.
• Out-of-band enforcement operates asynchronously, observing runtime behavior via logs, traces, or event streams. While this approach reduces operational risk and performance impact, it is primarily detective rather than preventative and may allow harmful actions to complete before intervention.

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In practice, high-risk actions (tool invocation, data access, code execution) benefit from inline controls, while broader behavioral analysis and drift detection often operate out-of-band.

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API-Level and Model-Level Coverage

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AI runtime security can be applied at multiple architectural layers, each offering different visibility and control.

• API-level coverage focuses on securing the interaction surface: prompts, responses, tool calls, and integrations. This layer is model-agnostic and scales well across heterogeneous environments, but it may have limited insight into internal reasoning or planning steps.
• Model-level coverage operates closer to inference execution, enabling deeper inspection of intermediate artifacts, system prompts, and context assembly. This provides richer behavioral signals but can be harder to standardize across different models, providers, and deployment modes.

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Effective runtime security architectures typically combine both, using API-level controls for consistency and breadth, and model-level hooks where deeper introspection is required.

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Protecting Cloud-Based and Self-Hosted AI Applications

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Deployment models introduce additional architectural considerations.

• Cloud-hosted AI applications rely heavily on managed services, third-party APIs, and shared infrastructure. Runtime security in these environments must integrate cleanly with identity providers, cloud networking, and logging systems, while respecting provider boundaries and service limits.
• Self-hosted or private-cloud deployments offer greater control over models, memory, and execution environments, but shift more responsibility to internal teams. Runtime security must account for model lifecycle management, patching, and isolation between tenants or applications.

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In both cases, the goal remains the same: enforce consistent runtime controls across environments without fragmenting security posture or creating blind spots as workloads move between cloud and on-premises infrastructure.

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Operationalizing AI Runtime Security in Production

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Moving AI runtime security from concept to practice requires embedding controls directly into production workflows without disrupting performance, reliability, or development velocity. The challenge is how to deploy them in a way that scales operationally.

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Integrating Runtime Controls into AI Application Workflows

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Runtime security is most effective when it is integrated into existing AI application paths rather than bolted on as a separate system.

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In practice, this means placing controls:

• Along inference paths where prompts, context, and outputs flow
• At tool invocation boundaries where actions are triggered
• At data access points where sensitivity and permissions matter

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Tight integration ensures that runtime policies are enforced consistently across applications and agents, without requiring developers to redesign application logic or duplicate security logic at each integration point.

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Balancing Latency, User Experience, and Enforcement

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Because runtime controls sit close to execution, they introduce legitimate concerns around latency and user experience.

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Production-grade runtime security must apply enforcement selectively based on risk and action type, and avoid full blocking for low-risk interactions. They also need to fail safely under load or partial outages.

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The goal is not maximum inspection everywhere, but proportionate enforcement that protects high-risk actions while keeping routine interactions fast and responsive.

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Scaling Runtime Security Across Teams and AI Applications

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As organizations deploy more GenAI applications, runtime security must scale horizontally without fragmenting governance.

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This requires:

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Centralized policy definition with decentralized enforcement

Security and risk teams need a single source of truth for policy, while engineering and platform teams need the freedom to enforce those policies locally without blocking delivery or introducing brittle dependencies.

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Consistent telemetry across models, teams, and environments

Security operations and AI risk teams rely on standardized telemetry to detect abuse, drift, and anomalous behavior, regardless of which model, framework, or team owns the application.

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Shared visibility for security, risk, and engineering stakeholders

Compliance teams need auditability, security teams need investigation context, and engineering teams need actionable feedback to fix issues without guesswork.

Without this, runtime security quickly breaks down into siloes, increasingly difficult to audit.

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Representative Runtime Security Use Cases

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Agentic workflow automation

Securing autonomous agents that plan and execute multi-step tasks, invoke tools, and act under delegated authority. The goal is to prevent hijacking, tool misuse, and unintended action execution at runtime.

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RAG-based internal assistants

Governing how AI applications retrieve, combine, and act on internal knowledge, with runtime controls to prevent memory poisoning, oversharing, and unauthorized data access during inference.

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Copilot-style productivity tools

Enforcing least-privilege access and continuous monitoring for AI assistants embedded in business workflows, where outputs may trigger downstream actions or influence human decision-making.

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Customer-facing AI applications

Monitoring and constraining live interactions to prevent abuse, data leakage, and policy violations without degrading user experience or blocking legitimate use.

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AI Runtime Security from a Compliance and Risk Team Lens

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For compliance and risk teams, AI runtime security is less about preventing every failure and more about ensuring visibility, control, and defensibility when failures occur. As GenAI applications and agents make autonomous decisions in production, traditional documentation and design-time controls are no longer enough to demonstrate compliance or manage risk.

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Runtime security provides the operational evidence needed to support governance, oversight, and accountability for live AI behavior.

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Audit Evidence and Traceability for AI Decisions

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Regulators and auditors increasingly expect organizations to explain how and why AI-driven decisions were made, not just how systems were designed.

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AI runtime security enables this by capturing:

• The inputs, context, and retrieved data that influenced a decision
• The policies and permissions in effect at execution time
• The actions the AI model took, including tool calls and data access

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This creates decision-level traceability that static model documentation or pre-release testing cannot provide, especially in stateful or agentic workflows.

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Supporting AI Governance and Risk Management Programs

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AI governance frameworks rely on consistent enforcement of policies across models, applications, and use cases. Runtime security turns governance from a policy document into an enforceable control layer.

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By applying policies dynamically based on context, identity, and risk, runtime controls help ensure that AI behavior stays within defined risk thresholds. They also ensure that high-risk actions trigger additional scrutiny or restriction.

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This is particularly important for managing agentic risks, where goal drift, tool misuse, or memory poisoning can undermine governance assumptions over time.

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Incident Investigation, Forensics, and Accountability

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When AI-related incidents occur, the critical questions are operational:

• What did the AI do?
• Under whose authority?
• Using which data and tools?
• At what point did controls fail or get bypassed?

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Runtime security provides the forensic record necessary to answer these questions with precision. Detailed execution logs, policy evaluations, and behavioral timelines allow teams to reconstruct incidents and demonstrate due diligence to regulators.

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Without runtime visibility, AI incidents are difficult to investigate and even harder to defend.

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Best Practices for Implementing AI Runtime Security

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AI runtime security is an operational discipline that assumes AI applications and agents will behave unpredictably once deployed. Because of that, risk management must be continuous. 

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The table below outlines core best practices for securing GenAI applications and agentic workflows in production.

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Key Features of AI Runtime Security Solutions

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AI runtime security solutions are ultimately evaluated by how they operate under live conditions. At a minimum, effective platforms provide continuous runtime visibility across inputs, context, outputs, and actions, paired with enforcement mechanisms that can intervene before high-risk behavior causes impact.

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Core capabilities typically include execution-time inspection, context-aware policy evaluation, fine-grained control over tool and data access, and persistent logging for audit and investigation. Just as importantly, these features must operate with low latency, integrate cleanly into existing AI architectures, and remain adaptable as applications, agents, and risk profiles evolve post-deployment.

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AI Runtime Security from Lasso’s Real-Time Protection Model

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Lasso approaches AI runtime security as a real-time control plane, designed to operate directly within live GenAI application flows. Rather than relying solely on preconfigured guardrails or post-hoc analysis, Lasso focuses on enforcing security policies at the moment models make decisions and take action.

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This model emphasizes continuous inspection, contextual policy enforcement, and automated response across AI applications and agentic workflows. By grounding security in runtime behavior, Lasso’s approach aligns runtime protection with how GenAI systems actually operate in production: dynamically, statefully, and at scale. To understand how real-time runtime protection is applied in live GenAI environments, teams can book a walkthrough with Lasso.

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Conclusion

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As GenAI applications and agents move from experimentation to core business infrastructure, security assumptions built for static, deterministic systems no longer hold. The most consequential risks emerge at runtime, when models reason, act, and interact with real data and systems.

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AI runtime security addresses this gap by shifting protection to where behavior actually unfolds. For organizations deploying GenAI at scale, runtime security is the foundation for safe operation, effective governance, and defensible use of autonomous AI in production.