Beyond SOC: Why trust in AI agents requires a new assurance  model

The 2025 enterprise landscape has moved past the era where

Software as a Service

and traditional audit frameworks could guarantee that software behaved as expected. Large language model (LLM) agents—capable of autonomous decision‑making, tool use, and self‑learning—now drive core business processes from customer support to finance and security operations. For executives, this shift is not merely technical; it is a macroeconomic transformation that reshapes risk profiles, regulatory compliance, and competitive advantage.

• Agentic autonomy introduces dynamic risk. Static SOC 1/SOC 2 audits cannot capture emergent decision logic.

• Policy‑as‑Code is the new compliance engine. Natural‑language policy authoring allows real‑time enforcement of tool access and action limits.

• Behavioral audit becomes a core pillar. Continuous testing against realistic adversarial scenarios is essential to mitigate prompt injection, backdoors, and inter‑agent exploitation.

• SOC must evolve into an Agent Assurance Center. Future SOCs will monitor autonomous workflows, integrate policy engines, and provide dynamic risk scoring.

• Strategic investment in agent assurance yields measurable ROI. Early adopters can reduce compliance costs, accelerate time‑to‑market, and unlock new revenue streams through AI‑driven services.

The global AI economy grew from $80 billion in 2023 to an estimated $190 billion by the end of 2025. Within this surge, agent‑centric solutions account for roughly 35% of spend—an increase driven by:

• Enterprise demand for intelligent automation that can handle complex, unstructured tasks.

• Regulatory pressure to demonstrate real‑time compliance in data‑sensitive industries such as finance and healthcare.

• The maturation of cloud AI platforms (Amazon Bedrock, Anthropic, Google Gemini) that expose policy APIs and logging hooks.

These dynamics create a new economic category:

Agent Assurance Services

. Just as traditional IT security has evolved into cybersecurity operations centers, the next wave will be Agent Assurance Centers (AACs), offering continuous monitoring, behavioral analytics, and policy enforcement for autonomous workflows.

Regulators in 2025 are grappling with how to apply existing frameworks—GDPR, CCPA, SOX—to systems whose behavior is not fully codified. The European Data Protection Board issued a guidance memo in March 2025 stating that “AI agents must be subject to the same transparency and accountability standards as human decision‑makers.” Yet, SOC 1/SOC 2 reports focus on code quality, infrastructure uptime, and data governance—all static dimensions.

Policy‑as‑Code bridges this gap by allowing enterprises to encode compliance requirements in plain English. Amazon Bedrock’s Policy feature demonstrates how a policy such as “Agent may only read customer emails that contain the keyword ‘invoice’” can be enforced in milliseconds. This real‑time check provides an audit trail that regulators can interrogate without needing to inspect model weights or training data.

Regulatory bodies are beginning to issue

policy‑enforcement certificates

, analogous to SOC reports, for systems that integrate policy engines. Early adopters of such certificates position themselves favorably in markets where compliance is a differentiator—financial services, insurance underwriting, and public sector procurement.

Agentic vulnerability rates are alarmingly high. A UC Davis survey published in October 2025 found that:

• 94.4% of state‑of‑the‑art LLM agents were susceptible to prompt injection.

• 83.3% had retrieval‑based backdoors.

• 100% could be coerced by other agents into disallowed actions.

These findings expose a fundamental flaw in static audits: they cannot detect emergent behaviors that arise only during operation. The TRiSM framework (Trust, Risk, and Security Management) proposes adding a third pillar—behavioral audit—to complement code quality and data governance.

Behavioral audit involves continuous monitoring of agent decisions, anomaly detection against baseline behavior, and adversarial testing in simulated operational contexts. Tools such as CompFly AI’s “agentic evaluations” embed these tests into CI/CD pipelines, ensuring that every deployment is vetted for decision logic before it reaches production.

Below is a pragmatic roadmap for enterprises looking to operationalize agent assurance. Each step links policy definition, enforcement, monitoring, and reporting.

• Identify regulatory requirements (GDPR Article 22, CCPA Section 3).

• Translate them into business rules (e.g., “No agent may access PHI unless role = ‘clinical reviewer’”).

• Use vendor APIs to encode policies in plain English.

• Example: “Agent can only call the payment API if transaction amount < $10,000 and customer is verified.”

• Deploy policy checks at the gateway layer; enforce in milliseconds.

• Ensure logs capture every decision point for auditability.

• Run synthetic workloads that mimic real user interactions.

• Record normal decision patterns and flag deviations.

• Automate prompt injection scenarios, backdoor activation tests, and inter‑agent coercion simulations.

• Use tools like CompFly AI to surface latent vulnerabilities before they hit production.

• Assign risk weights to policy violations (e.g., “unauthorized data access” = 9/10).

• Feed scores into a real‑time dashboard for SOC analysts and CCOs.

• Compile logs, risk scores, and audit evidence into a certificate format accepted by regulators.

• Leverage vendor SDKs to auto‑populate compliance reports.

• Leverage vendor SDKs to auto‑populate compliance reports.

The agent assurance market is fragmenting along three axes:

• Platform‑as‑Service (PaaS) : Amazon Bedrock, Anthropic, Google Gemini offer policy APIs and logging hooks. They cater to enterprises that want to build custom agents without managing infrastructure.

• Assurance-as-a-Service (AaaS) : Startups like CompFly AI, TrustForge, and VerityAI provide end‑to‑end behavioral testing, policy enforcement engines, and audit certification services.

• Enterprise SOC Extensions : Traditional SOC vendors (IBM Security, Palo Alto Networks) are integrating agent monitoring modules into their platforms to offer “SOC+Agent Assurance.”

Revenue projections for the Agent Assurance segment suggest a compound annual growth rate (CAGR) of 28% from 2025 to 2030, driven by:

• Increasing regulatory scrutiny.

• The cost savings of automated compliance versus manual audits.

• The monetization potential of AI‑driven services in regulated sectors.

A case study from a mid‑size financial institution that adopted policy‑as‑code for its credit‑scoring agent shows:

• Compliance cost reduction: 45% lower than traditional audit cycles.

• Time‑to‑market acceleration: 30% faster deployment of new features due to automated behavioral testing.

• Risk mitigation savings: Estimated $2.3 million avoided in potential regulatory fines over three years.

Key drivers of ROI include:

• The ability to perform continuous compliance checks without additional human oversight.

• Reduction in incident response time thanks to real‑time risk scoring.

• Enabling new revenue streams through agent‑powered services that can be offered to customers under regulatory guarantees.

• Embed policy‑as‑code and behavioral audit into the enterprise architecture roadmap.

• Allocate a dedicated budget line for agent assurance tools and services.

• Form an AI Risk Committee that includes legal, compliance, security, and product teams.

• Mandate quarterly reviews of policy enforcement logs and risk scores.

• Select vendors with open policy languages to avoid lock‑in.

• Negotiate service level agreements (SLAs) that include real‑time compliance reporting.

• Hire AI ethics officers and behavioral analysts.

• Provide continuous education on policy‑as‑code best practices for developers.

• Run pilot agents in controlled environments with full monitoring before scaling to production.

• Use results from pilots to refine policies and risk models.

• Use results from pilots to refine policies and risk models.

The trajectory of agent assurance suggests several macro trends:

• Standardization of Agent Decision Logs : Industry consortia are working toward a vendor‑agnostic schema, enabling cross‑platform audits.

• Emergence of AI Governance Marketplaces : Platforms where enterprises can purchase pre‑validated policy bundles for specific verticals (healthcare, finance).

• Integration with Enterprise Risk Management (ERM) : Agent risk scores will feed into enterprise dashboards, aligning AI operations with broader risk appetite frameworks.

• Regulatory Evolution : Bodies like the European Commission are expected to issue formal guidelines for “AI Agent Assurance Certificates” by 2027, making them a prerequisite for market entry in regulated sectors.

The shift from static compliance audits to dynamic, policy‑driven assurance is not optional—it is inevitable. Enterprises that recognize the economic value of agent assurance and act decisively will:

• Reduce regulatory exposure and associated financial penalties.

• Accelerate innovation cycles by removing manual compliance bottlenecks.

• Differentiate themselves in markets where trust is a competitive moat.

In 2025, the next wave of digital transformation hinges on agents that are not only intelligent but also auditable and compliant in real time. The strategic investments outlined here provide a roadmap for leaders to navigate this new frontier and secure lasting value from AI‑driven operations.