AI Security Posture Management: Why Runtime Detection Completes the Security Strategy

As organizations deploy AI models, autonomous agents, RAG applications, machine identities, and AI-enabled workflows, the AI attack surface is expanding rapidly. Security teams need visibility into what AI assets exist, how they are configured, and where security risks are concentrated.

This is the role of AI Security Posture Management (AI-SPM).

However, posture management addresses only part of the security problem. A system can be correctly configured and still be actively targeted by an attacker using stolen credentials, compromised identities, manipulated agents, or legitimate permissions.

This creates an important distinction between security posture and runtime security.

AI-SPM helps answer:

What AI assets do we have, how are they configured, and where are the risks?

Runtime detection addresses a different question:

Is someone actively abusing those assets or trusted identities right now?

For modern AI environments, both questions matter.

At a Glance

• AI Security Posture Management provides inventory, configuration, permission, and risk visibility across AI environments.
• AI-SPM is primarily a preventive capability: it identifies weaknesses before they are exploited.
• A correctly configured AI system can still be compromised through stolen credentials, excessive permissions, manipulated agents, poisoned context, or abused APIs.
• Runtime detection identifies active malicious activity after authentication or authorization has already succeeded.
• Cyber deception creates high-confidence detection opportunities by deploying deceptive identities, credentials, services, and other assets that legitimate workflows should never access.
• Acalvio 360 Deception extends runtime protection across AI-accessible attack paths, while ShadowPlex provides automated deception capabilities for enterprise and cloud environments.

What Is AI Security Posture Management?

AI Security Posture Management (AI-SPM) is designed to continuously discover AI assets, assess their configurations against security policies, identify excessive permissions, and prioritize risks across the AI attack surface.

The concept is similar to Cloud Security Posture Management (CSPM), but extends security visibility to AI-specific components such as:

• AI models and model endpoints
• AI agents
• Machine identities
• API integrations
• RAG applications
• Vector databases
• AI development environments
• Cloud AI services
• Data repositories supporting AI workloads

As enterprises introduce more autonomous agents and AI-driven applications, manually maintaining an accurate inventory becomes increasingly difficult.

AI-SPM helps security teams establish that visibility and identify configuration weaknesses before they become exploitable attack paths.

That is a genuine and important security function.

However, AI-SPM primarily evaluates configuration and posture state. It is not designed to determine whether a legitimate identity is currently being used for a malicious purpose.

This distinction creates the runtime detection gap.

The Runtime Detection Gap

Modern attackers increasingly rely on legitimate access rather than attempting to break authentication mechanisms.

An adversary may obtain:

• stolen credentials;
• compromised service accounts;
• OAuth or API tokens;
• excessive permissions;
• cloud identities;
• machine identities;
• access to an AI agent;
• compromised third-party tools or integrations.

Once authenticated, the attacker may operate using permissions that the organization intentionally granted.

From a posture-management perspective, the environment can remain correctly configured.

The identity is valid.

The permissions are valid.

The API call is authorized.

Yet the activity may still be malicious.

This is the post-authentication judgment gap.

AI-SPM can tell security teams that an identity has access to an AI service. It does not necessarily determine whether that identity is using the access for its intended business purpose.

This is why posture management and runtime detection should be treated as complementary capabilities rather than competing approaches.

How Runtime Detection Complements AI-SPM

AI-SPM provides a map of the environment and highlights areas where exposure is concentrated.

Runtime detection provides evidence that those assets are actually being targeted or misused.

Together, they create a more complete security architecture.

Cyber deception provides one way to generate high-confidence runtime signals.

Instead of relying exclusively on behavioral anomalies, defenders can deploy deceptive credentials, honeytokens, decoy services, HoneyPaths, and other realistic assets along attack paths that legitimate users and applications have no reason to access.

When an attacker interacts with one of these assets, the interaction itself becomes a security signal.

The question changes from:

"Does this behavior look suspicious?"

to:

"Why did an identity or process interact with an asset that no legitimate workflow should ever need?"

That distinction is particularly important in AI environments, where autonomous agents can generate large volumes of legitimate-looking activity.

Using AI-SPM Findings to Prioritize Deception

AI-SPM can also help determine where deception should be deployed first.

For example, posture analysis may identify:

• a machine identity with access to multiple AI services;
• an API connected to sensitive datasets;
• an exposed AI endpoint;
• a high-value vector database;
• a privileged service account;
• a cloud storage location containing sensitive AI artifacts.

These findings can be used to identify the attack paths where deceptive assets are most valuable.

Rather than distributing deception uniformly, security teams can focus coverage on the identities, systems, and pathways most likely to be targeted.

Four Layers of an AI Security Architecture

A mature AI security program requires multiple complementary controls.

1. AI asset inventory and posture management — discovers AI assets and continuously evaluates configuration and exposure.
2. Configuration and identity governance — applies IAM, least privilege, authentication, and secure development controls.
3. Runtime threat detection — identifies active attacks and misuse after authentication or authorization.
4. Response and investigation — integrates security signals into SIEM, SOAR, and incident-response processes.

Cyber deception adds a high-confidence detection layer to this architecture.

It does not replace IAM, PAM, MFA, EDR, XDR, SIEM, or AI-SPM. Instead, it provides additional evidence that can help those systems identify malicious activity earlier.

Why AI Environments Make Runtime Detection More Important

Traditional security analytics frequently rely on establishing a baseline of normal activity and identifying deviations.

This becomes increasingly difficult as AI workloads become more dynamic.

An autonomous agent may legitimately:

• call different APIs depending on the task;
• access different datasets;
• interact with multiple tools;
• create temporary resources;
• execute variable sequences of actions;
• operate continuously without direct human intervention.

Consequently, an unusual action does not automatically indicate malicious behavior.

At the same time, a malicious action may appear perfectly legitimate when performed using valid credentials.

This creates a difficult detection problem:

How can security teams distinguish authorized activity from authorized activity being used for an unauthorized objective?

Deception offers another source of evidence.

If an AI agent, service account, or compromised identity accesses a credential, API, system, or resource that has no legitimate business purpose, the interaction can provide a much stronger indication of malicious activity.

Acalvio 360 Deception: Adding Runtime Intelligence

Acalvio 360 Deception extends cyber deception beyond isolated honeypots and static decoys.

The approach combines multiple forms of deception designed to interfere with attacker reconnaissance and expose malicious activity, including:

• deceptive identities and credentials;
• honeytokens;
• decoy services and infrastructure;
• deceptive attack paths;
• HoneyPaths;
• cloud resources;
• endpoint and network deception;
• AI-accessible deceptive assets.

The objective is to create an environment in which attackers cannot easily establish reliable ground truth about the infrastructure.

This is particularly relevant for automated attacks.

Autonomous attack systems depend on information about the environment to determine what to target, which credentials to use, and where to move next. Deceptive assets introduce uncertainty into that decision process while creating additional opportunities for detection.

For cloud-native AI environments, ShadowPlex Cloud Security can extend deception across cloud resources and attack paths where adversaries may search for credentials, secrets, storage, and privileged access.

What the Navy ANTX FY25 Exercise Demonstrated

Acalvio's deception technology was evaluated during the U.S. Navy NIWC Pacific Cyber Resilient Systems ANTX FY25 exercise against a sophisticated red team operating in a live adversarial environment.

According to Acalvio, the deployment generated 100% true-positive alerts and denied approximately 80% of attacker objectives within the exercise environment.

These figures represent different measurements.

A 100% true-positive rate means that alerts generated by the deception layer corresponded to confirmed malicious activity. It does not mean that every possible intrusion scenario was detected.

The approximately 80% objective-denial figure measures the operational effect of the deception deployment within the exercise.

The reported detections covered activities including:

• reconnaissance and enumeration;
• credential access;
• lateral movement;
• privilege escalation;
• data exfiltration.

The exercise illustrates the role deception can play as a runtime detection layer alongside conventional security controls.

AI-SPM and Deception: Complementary Security Capabilities

Security Capability	Primary Question	Security Function
AI Security Posture Management	What AI assets exist and how are they configured?	Inventory, configuration assessment, exposure management, and risk prioritization
IAM and Identity Governance	Who is authorized to access resources?	Identity management, authentication, authorization, and least privilege
Runtime Detection	Is an identity, agent, or process currently behaving maliciously?	Detection of active compromise and post-authentication abuse
Cyber Deception	Did an attacker interact with something that should never be accessed?	High-confidence detection through deceptive assets and attack paths
SIEM/SOAR	How should security events be correlated and acted upon?	Investigation, orchestration, response, and incident management

The objective is not to replace one capability with another.

Instead, organizations can use AI-SPM to identify where their AI security posture requires attention and runtime deception to identify when those environments are actively being targeted or misused.

AI Security Governance Requires Continuous Monitoring

AI security frameworks increasingly emphasize that risk management cannot stop at configuration assessment.

The NIST AI Risk Management Framework (AI RMF) organizes AI risk management around four functions: Govern, Map, Measure, and Manage. Continuous monitoring and risk management are important components of maintaining AI security as systems and threats evolve.

Similarly, the OWASP Top 10 for LLM Applications addresses risks such as prompt injection, excessive agency, insecure output handling, and supply-chain vulnerabilities.

These risks require both preventive and runtime controls.

Configuration management can reduce exposure.

Identity controls can restrict access.

Model and application guardrails can constrain AI behavior.

Runtime detection can identify malicious activity that occurs despite those controls.

Cyber deception adds another mechanism by creating controlled interaction points that can expose unauthorized activity with high confidence.

Building an AI-Ready Security Monitoring Strategy

Organizations deploying AI agents and AI-enabled applications should consider the following capabilities as part of a layered security architecture:

1. Discover the AI Attack Surface

Maintain an up-to-date inventory of models, agents, APIs, machine identities, data stores, RAG systems, tools, and external integrations.

2. Assess Configuration and Permissions

Use AI-SPM and identity governance to identify excessive permissions, exposed services, insecure configurations, and unnecessary trust relationships.

3. Monitor AI Runtime Activity

Establish visibility into agent actions, API calls, identity usage, tool invocation, data access, and other runtime activity.

4. Deploy High-Confidence Detection

Use cyber deception to create deceptive credentials, honeytokens, decoy services, and HoneyPaths across high-value attack paths.

5. Integrate With the SOC

Forward deception alerts and runtime signals into existing SIEM, SOAR, XDR, and incident-response workflows.

6. Validate the Architecture

Regularly test detection and response using adversary simulation, attack-path analysis, and AI-assisted attack scenarios.

The objective is to create continuity between exposure management and active threat detection.

AI Security Posture Management Plus Runtime Detection

AI-SPM is becoming an important component of enterprise AI security because organizations cannot protect assets they cannot identify or understand.

But knowing that an AI environment is properly configured does not prove that it is secure at runtime.

A legitimate identity can be compromised.

An authorized API can be abused.

An AI agent can be manipulated.

A service account can be redirected toward an unauthorized objective.

In each case, the configuration may remain unchanged while the security situation changes dramatically.

This is why AI security requires both posture visibility and runtime detection.

AI-SPM helps organizations understand what exists, how it is configured, and where exposure is concentrated. Runtime detection helps determine whether attackers are actively exploiting that environment.

By adding cyber deception to the security architecture, organizations can create high-confidence detection opportunities across AI-accessible attack paths and strengthen existing investments in IAM, PAM, EDR, SIEM, XDR, and cloud security.

Strengthen Your AI Security Strategy With Acalvio

As AI agents and autonomous workflows become more deeply integrated into enterprise environments, security teams need visibility not only into how AI systems are configured, but also into how they behave under attack.

Acalvio's 360 Deception and ShadowPlex technologies provide deception-based runtime detection designed to expose credential abuse, lateral movement, unauthorized access, and other malicious activity across enterprise, cloud, identity, and AI environments.

Softprom is an official distributor of Acalvio and can help organizations evaluate how deception-based runtime detection can complement their existing AI-SPM and security architecture.

Explore how Acalvio can help close the gap between AI security posture and runtime threat detection.