Why Continuous, Context-Aware & Runtime Detection is a pre-requisite for AI Agent Adoption

Securely Scale AI Agents with Runtime AI Detection 

AI agents are redefining enterprise automation. By autonomously querying databases, invoking APIs, triggering workflows, and modifying live configurations, they compress what once took hours of human coordination into seconds.

The impact is measurable: faster reconciliations in finance, automated care coordination in healthcare, and up to 90% faster ticket resolutions across IT and SaaS operations. 

But the same runtime access that enables this efficiency also forms the core of the security risk.  A single mis-scoped permission or poisoned prompt can cascade into a data breach or configuration drift in milliseconds.

This is why AI-native runtime detection is non-negotiable. It delivers visibility, enforcement, and response at the point of execution, ensuring every agent action aligns with enterprise policy and intent.

Without it, enterprises lose visibility, control, and ultimately, operational velocity.

Resources and Functionalities: What it Actually means for the AI Agents

To manage risk where it actually appears, start with understanding what ‘access’ really means for an agent in production.

AI agents are winning across industries because they don’t just advise but act. Acting means they plug into the same systems your teams use every day i.e. databases, payment platforms, identity providers, scheduling tools, and internal APIs. That’s what turns them into force-multipliers for staff and operations.

AI agents accessing customer databases can be highly productive when properly scoped. For instance, a support agent using a read-only token can retrieve order history and open tickets to suggest relevant solutions or expedite refunds, improving customer experience and reducing manual workload. 

However, the same access can become risky if the agent is granted broad write permissions. In that case, a misconfigured workflow or malicious prompt could cause the agent to overwrite order records, modify billing data, trigger unauthorized refunds, or inadvertently expose sensitive customer information. 

This highlights that access itself is neutral, the difference between productivity and risk depends on proper scoping, context awareness, and runtime controls.

What “Access” to AI Agents Really Means

Runtime Access is granted to AI agents with autonomous decision-making to function independently. AI Agents can access Resources and Functionalities during runtime. At runtime, AI agents operate as autonomous service principals with access to a combination of resources and functionalities, which they leverage to execute tasks dynamically.

Resources are the underlying systems and data stores that agents can reach. These include:

• Customer databases: Agents query or update structured data such as user profiles, transaction histories, or CRM records. For example, an agent handling support tickets might fetch a customer’s purchase history from a relational database using SQL queries executed through a service account with restricted read/write privileges.
  
  
• Vector and file stores: Agents access unstructured or semi-structured data, like embeddings for semantic search, PDFs, or logs stored in object storage systems. For instance, a recommendation agent might retrieve vector embeddings from a Pinecone or FAISS index to generate personalized suggestions.
  
  
• SaaS apps, internal APIs, and payment systems: Agents can interact with SaaS platforms (example, Salesforce, Jira) or internal APIs to trigger workflows or fetch operational data. For example, a billing agent might call a payment gateway API to initiate refunds or reconcile invoices.

Functionalities define the actions agents can perform once they have access:

• Create or update records: Agents can insert, modify, or delete entries in databases or SaaS systems, such as updating customer subscription tiers.
  
  
• Trigger refunds or workflows: Through API calls, agents can initiate multi-step processes like automated ticket resolution or financial transactions.
  
  
• Modify configurations, send emails, or spawn sub-agents: Agents can update system configurations, send notifications, or even orchestrate new agent instances or switch between language models to handle specialized tasks.

How access is provisioned and enforced in the backend

• Delegated credentials or scoped tokens: Agents often operate using OAuth tokens, JWTs, or short-lived API keys with explicit scope, allowing controlled access to specific endpoints or datasets. For example, a token might grant read-only access to a customer table while preventing modification of billing records.
  
  
• Workload identities: These are service accounts representing agents or workflows acting on behalf of users or other services. Kubernetes service accounts or AWS IAM roles are typical examples, enabling agents to perform actions with auditable identity attribution.
  
  
• Tool schemas and MCP interfaces: Agents interface with predefined tool schemas or Multi-Channel Pipeline (MCP) APIs, which abstract complex backend functionality behind simple prompts. For instance, a prompt like “Refund last failed payment” is translated into a sequence of authenticated API calls and validations on the backend.
  
  
• Persistent memory and session context: Agents maintain runtime state or session memory to inform multi-step tasks. For example, in a multi-turn conversation with a customer, the agent can carry forward prior authentication tokens, selected account details, or workflow progress to make context-aware decisions across steps.

By combining these elements, agents act as autonomous, policy-bound orchestrators of backend operations, dynamically executing complex workflows while continuously interacting with systems and data. Without proper scoping, auditing, and runtime controls, this level of access can amplify risk, allowing unintended modifications or data exposure at machine speed.

When AI Agents become Risky

But the very same access that fuels the business case is also the attack surface.

Agents don’t run from static playbooks. Their plans are generated dynamically, based on inputs that can be ambiguous, misleading, or even malicious. Execution chains evolve mid-flight, crossing multiple systems. And often, there’s no human in the loop when the critical action fires.

This creates new classes of runtime risk:

• An agent with overly broad permissions can make unintended, high-impact changes:
  Agents with excessive access can execute actions beyond their intended scope. For example, a DevOps automation agent with full API rights across production environments could overwrite critical configurations or escalate privileges within dependent systems. Mitigation requires role-based access controls and scoped, ephemeral tokens per task.
  
  
• Poisoned inputs can manipulate outcomes or leak sensitive data:
  Adversarial or crafted inputs can subvert agent logic. A summarization agent with database access might be tricked into returning PII. Without input validation and output filtering, poisoned prompts can corrupt workflows, manipulate analytics, or leak sensitive information. Runtime validation and anomaly detection are essential counter measures.
  
  
• Sub-agents or clones can silently inherit access and proliferate risk
  Orchestrated sub-agents often inherit parent privileges by default. Multiple analytical sub-agents with inherited access to financial ledgers can multiply risk by executing unintended queries or replicating sensitive data. Containment requires explicit inheritance control, ephemeral credentials, and detailed audit trails.
  
  
• Session memory can carry forward unintended context across actions
  Persistent session memory can propagate sensitive context across tasks. A multi-turn agent retaining prior authentication tokens or configuration parameters may expose data or trigger unintended API calls in subsequent tasks. Mitigation involves strict memory scoping, session isolation, context resets, and anomaly detection.

The Runtime Dangers of Over Permissive Agents

Over permissive agents when in chain get broader access and often lead to runtime risks which might not seem like a danger in standalone capacity but appears in a larger more connected ecosystem. 

1. Data Exfiltration from Live Workflows

Data exfiltration in AI agent workflows often happens silently, in the background of live systems that were never designed to handle autonomous decision-making. An agent can be tricked through prompt manipulation, poisoned training data, or poorly configured connectors into retrieving and exposing information far beyond its intended scope.

Consider a healthcare summarization agent tasked with extracting details from a single patient visit. Instead of stopping at one record, it might pull an entire dataset from the electronic health record system.

These incidents unfold in real time, often beyond the reach of traditional logging, access monitoring, or data loss prevention tools. As a result, the data movement goes unnoticed while sensitive information quietly crosses compliance boundaries.

The consequences can be severe. The risk isn’t just unauthorized access but invisible data flow. Regulated or proprietary data leaving approved systems without human oversight can trigger compliance violations, breach notifications, financial penalties, and long-term damage to customer trust.

Imagine a customer support agent designed to view only basic order details receives a cleverly crafted prompt that leads it to reveal complete credit card numbers. With no runtime guardrails or contextual validation, the agent follows the instruction and exposes sensitive data externally.

Agentic autonomy creates a new class of exfiltration: silent, compliant, and invisible to traditional security tooling. Agentic systems are highly capable, fast, but dangerously compliant when left without proactive controls.

2. Privilege Escalation and Scope Creep

In complex agentic environments, privilege escalation doesn’t always come from an exploit. It often arises from how agents chain actions and combine access paths during runtime. When multiple systems and roles converge, the agent’s effective authority can grow far beyond its original scope.

An agent with permissions for both internal tools and version control might start interacting with public and private repositories at once. Others may spawn sub-agents or dynamically switch tools mid-task, gaining access to environments they were never meant to control. These behaviors often go unnoticed because they appear as valid task execution.

The danger lies in scope creep. Over time, an agent can evolve into a de facto “machine admin,” executing deletions, approvals, or configuration changes that no single role was ever designed to perform. It’s the AI equivalent of a shadow admin account created unintentionally through delegated logic.

Consider a finance agent cleared to process refunds up to $100. If it misreads context or chains tool permissions incorrectly, it could approve a $10,000 transaction. The system doesn’t see this as a hack, it sees it as the agent doing its job.

Unchecked autonomy turns convenience into control. Without scoped permissions, behavioral baselines, and runtime enforcement, agents can escalate privileges faster than traditional systems ever could.

3. Persistent or Rogue Agents

Persistent or rogue agents are a subtle but serious risk in autonomous systems.  These agents do not need to be malicious to become dangerous; they simply continue operating beyond their intended purpose. When agents have long-running sessions, recursive logic, or the ability to spawn sub-agents, they often outlive their intended purpose.

A background agent may keep performing actions after its task ends, or a sub-agent may inherit privileges and continue operating unseen. Without strict lifecycle and privilege controls, these processes remain active with system-level access.

The consequences go well beyond wasted compute. The result is silent drift and escalating risk. Persistent agents can alter configurations, revoke access, or trigger actions based on outdated context. They consume resources, disrupt operations, and create blind spots that resemble persistent malware, only they are doing exactly what they were told, just without oversight.

Imagine an IT agent tasked with monitoring logins. If its session never ends, it might keep issuing resets or revoking user accounts for hours or days, believing it is still responding to threats.

Without runtime governance and enforced termination rules, persistence becomes a silent failure mode of autonomy, an agent that keeps acting even when no one is watching.

4. Lateral Movement Across Agents and Systems

Lateral movement is no longer limited to human attackers. In multi-agent environments, a single compromised or misdirected agent can become the entry point for systemic drift. Once an attacker or poisoned instruction finds a foothold, it can move laterally through delegated tasks, shared memory, and tool calls.

For instance, Agent A might have access to an internal database, while Agent B can connect to external systems. If A is compromised, it can pass sensitive data to B, which then unknowingly exports it. Each agent acts within its defined permissions, yet the combination produces a policy breach invisible to traditional controls.

This dynamic creates a stealthy, combinatorial path to system-wide exposure. Within seconds, a local issue escalates into a cascading failure. When agents collaborate without strong isolation or runtime validation, every shared interface becomes a potential bridge for lateral movement.

A simple example: a CRM agent shares customer data with a marketing agent for follow-up. Without context boundaries, that data could be emailed to the wrong audience, violating privacy rules and compliance standards in one automated step.

These Risks are Real and Already Emerging

Certain agent behaviors mirror well-known security failures like Identity and Access Management (IAM) misconfigurations, shadow admin accounts, or overly trusted service roles. These are now amplified by automation, speed, and the agent’s inability to understand policy boundaries. Hence the risk is real and critical.

These aren’t speculative edge cases. Early enterprise trials have documented agents:

• Accessing unauthorized repositories
• Suggesting circumventions of security policies
• Attempting to reach external APIs of competitors
• Spawning sub-agents that inherit parent privileges

Why Enterprises Need Runtime Guardrails for AI Agents Now

Without security guardrails, 32% of pilots stall at proof-of-concept, meaning competitors that solve security first get to scale faster and win market share.

Just as you wouldn’t hand a new employee the master key to every system, agents need limits, visibility, and runtime guardrails. Every time they take an action, it should be traceable:

• Who gave the instruction (user, process, or agent)
• What action was taken (and where)
• Why it was allowed (the policy or rule behind it)

Without this level of monitoring, agents become not just tools, but threats. Over-permissive agents act like tireless insiders with perfect access, little context, and no sense of when to stop.

The result? Data leaks. Financial loss. Policy violations. Silent escalation. And all of it at machine speed.

If risk emerges in the act, then protection must live in the act too.

Levo’s Runtime Detection for AI Agents

AI agents succeed because they act, but that action only benefits the business when it’s both safe and observable. Levo’s runtime detection makes this possible by delivering real-time, identity-aware threat-detection for ai agents operating in production, not just what they should do, but what they’re actually doing.

The result? Teams move fast with autonomous agents, without trading off control. Velocity stays with the business, not with the attacker.

How It Works: Seeing the Act, Not Guessing Around It

Levo doesn't rely on static configurations or after-the-fact logs. It embeds into the live runtime mesh i.e. the full system of agents, identities, tools, and data paths, where it analyzes every action with full context:

• Which agent is acting
• Under which identity and delegated token
• What resource or tool it's touching
• What data is flowing
• Whether it’s staying within its approved scope

By mapping these factors in real time and understanding how they interact mid-chain Levo surfaces true risks while minimizing noise. That means fewer false positives, faster containment, and no need to slow teams down with manual review.

What Levo Detects and Why It Matters

Not all agent behavior is risky but some of it becomes dangerous fast. Levo separates signal from noise by detecting the behaviors that matter most in production, with context-aware precision. Each detection point targets a specific runtime risk, stopping problems before they escalate.

Here’s what Levo detects:

1. Agent-to-Resource Access Validation

Agent-to-Resource Access Validation ensures every AI agent touches only the systems, APIs, and databases it is explicitly cleared to use. By tracking access paths in real time, it prevents unauthorized or overly broad actions, such as an agent pulling sensitive data from an internal system outside its mandate.

This level of validation enforces scope boundaries and reduces the risk of agents drifting into unintended or high-risk areas. It also stops silent data exfiltration before it happens, keeping automation aligned with governance requirements.

For the business, this means fewer compliance incidents, stronger data integrity, and sustained trust in autonomous operations, allowing automation to scale without sacrificing control.

2. Identity to Agent Access Validation

Identity to Agent Access Validation ensures that every agent action is tied back to a verified and authorized identity, whether human or machine. It confirms that each command originates from an approved source and that no shadow agents or rogue tokens can operate undetected.

This mechanism enforces accountability by mapping intent to identity, eliminating invisible insiders who could act under false roles or unmonitored credentials.

For enterprises, it strengthens audit integrity and removes ambiguity from agent-driven decisions, making every automated action traceable, verifiable, and compliant.

3. Session-Level Drift Enforcement

Session-Level Drift Enforcement monitors agent sessions to detect any changes in scope during a task, such as the introduction of new tools, escalation of capabilities, or unexpected persistence. By identifying these deviations, it prevents issues like recursive agent spawning, unintended privilege escalation, and long-running rogue sessions. 

This is critical because session drift can silently transform otherwise safe tasks into high-risk operations if left unchecked. From a business perspective, enforcing session-level controls helps contain the potential blast radius, stop runaway agents, and maintain overall production stability.

4. Data-Sensitivity Access Validation (AI DLP)

Data-Sensitivity Access Validation (AI DLP) monitors agent activity to detect and block any access or exposure of sensitive data, such as PII, PHI, or financial information, that violates policy. It prevents data leaks arising from poisoned prompts, unscoped retrievals, or insecure outputs. 

This capability is critical because sensitive data often flows through agents without visibility, and AI DLP provides real-time awareness to act before a breach occurs. From a business perspective, it helps avoid regulatory fines, ensures compliance with standards like HIPAA and GDPR, and protects user trust.

5. Region and Vendor Access Validation

Region and Vendor Access Validation tracks where agents send data, including which clouds, vendors, or geographic regions are involved. By monitoring this activity, it prevents violations of data residency rules, such as routing EU data to a U.S.-based model endpoint. 

This capability is important because enterprises often lack visibility into these flows in real time without automation. From a business perspective, it helps avoid regulatory delays, ensures data sovereignty, and secures operations across multi-cloud agent environments.

This means enterprises can keep agents acting for the business, while preventing those same actions from becoming attacker leverage.

The result?

• Secure scale
• Fewer humans in the loop
• Faster time from pilot to production

Beyond Runtime AI Detection , Complete AI Security

Levo extends beyond visibility to cover the full spectrum of AI security. Its breadth spans MCP servers, LLM applications, AI agents, and APIs, while its depth runs from shift-left capabilities like discovery and security testing to runtime functions such as monitoring, detection, and protection. By unifying these layers, Levo enables enterprises to scale AI safely, remain compliant, and deliver business value without delay.

Book a demo through this link to see it live in action!