Top 5 Reasons Agentic AI Can Be Unsafe (Even When Technically Secure) and How Trustwise Fixes it at Runtime

Agentic AI isn’t on the horizon; it’s already inside enterprise systems, making autonomous decisions, triggering real-world actions, and interacting with sensitive data in real time.

Enterprises have long battled shadow AI (unapproved tools adopted by employees outside governance). But the greater emerging threat is rogue AI (systems that are technically secure yet behave in unsafe, unpredictable or misaligned ways once deployed). Shadow AI is about who is using AI. Rogue AI is about how AI itself behaves. 

This distinction is extremely important. Conventional security investments – blocking malicious prompts, controlling access, preventing data exfiltration – protect against external compromise, but they do not guarantee safe outcomes. An AI agent can be “secure” by technical standards and still produce actions that violate business policy, regulatory mandates, or ethical norms. These failures often stem from misalignment, drift, or contextual blind spots in how the agent interprets and executes tasks.

Enterprises must evolve from securing systems at the perimeter to governing agent behavior at runtime to ensure AI is not only protected from external threats, but also verifiably aligned with organizational intent, internal policy and external regulations.

Here are five reasons why agentic AI can be unsafe even when technically “secure,” and how the multi-shield architecture within our Harmony AI platform addresses the challenges:

1. Hallucination chains. Agents act on hallucinations, triggering unsafe real-world consequences.

• Example: A medical triage agent invents a symptom, leading to misrouted patient care.
• The Trustwise fix: Action Shield intercepts unsafe fragments before execution. Trust Score diagnostics trace hallucination chains and block drift mid-flow.

2. Costly loops and runaway actions. Even secure models can spiral into inefficient execution that silently drains resources.

• Example: A research agent retries queries endlessly, racking up token and API charges.
• The Trustwise fix: Cost Shield sets per-agent ceilings, stops runaway loops, and routes workloads to efficient models, keeping budgets and carbon use in check.

3. Emergent misbehavior and deceptive actions. Agents improvise, sometimes masking intent, or chaining tools in unintended ways.

• Example: A financial agent splits actions across tools to bypass transaction limits.
• The Trustwise fix: MCP Shield enforces scope, validates toolchains, and blocks off-policy execution. The core of Harmony AI, the Control Tower, monitors drift and auto-adjusts thresholds.

4. Policy and compliance drift. Passing an audit once doesn’t guarantee long-term adherence. Agents degrade in production.

• Example: A KYC agent stops logging regulated advice, breaking FCA requirements.
• Trustwise Fix: Compliance Shield enforces 1,100+ mapped controls in real time (ISO, NIST, EU AI Act). Harmony AI logs every compliance-relevant transaction for audits.

5. Tool misuse & privilege escalation. Tools are the weakest link: even safe models inherit their vulnerabilities.

• Example: A poisoned CRM plugin initiates unauthorized fund transfers.
• Trustwise Fix: MCP Shield validates every tool call, quarantines unsafe connectors, and enforces zero trust execution boundaries.

Agentic AI doesn’t fail because it’s insecure. It fails because it’s unaware, unsupervised, or misaligned. Trustwise turns alignment from a design-time hope into a runtime guarantee.

Technically secure does not equal safety in production. Only runtime controls, like Harmony AI Shields and the platform’s AI Control Tower, make agents verifiable digital workers instead of unpredictable insider threats.

Trustwise Named a Cool Vendor in the 2025 Gartner® Cool Vendors™ for Agentic AI in Banking and Investment Services Report

By Manoj Saxena, CEO and founder of Trustwise

Our founding thesis at Trustwise was that organizations need AI systems that deliver innovation and accountability in equal measure. It’s an especially urgent priority for those operating in high-stakes environments, like financial services. We’re proud to announce that we’ve been named as a Cool Vendor in the Gartner Cool Vendors for Agentic AI in Banking and Investment Services. 

The Trust Gap in Finserv
Banks and investment firms are racing to deploy agentic AI systems that can operate autonomously, make real-time decisions, and interact with customers without human oversight. However, traditional safety and security tools were built for a different era. They assume attackers need to break through firewalls or exploit code vulnerabilities. AI agents operate on a completely different plane.

When your AI assistant can interpret natural language instructions and execute actions across your entire technology stack, every conversation becomes a potential attack vector. Every prompt becomes executable code. Every interaction with external systems becomes a trust decision that happens in milliseconds.

The financial services sector faces unique pressures in this landscape. Regulatory compliance isn’t optional, risk management isn’t negotiable, and operational safety and efficiency directly impacts customer trust and regulatory standing.

Why Runtime Protection Changes Everything
Consider this hypothetical scenario: a customer service AI agent at a bank receives what appears to be a routine inquiry about account information. Embedded within that seemingly innocent request are instructions that cause the agent to bypass its safety protocols and expose sensitive customer data. By the time the security team reviews the logs, the damage is done.

This is why runtime protection becomes essential. Unlike conventional security approaches that react after incidents occur, runtime policy intercepts, analyzes and validates AI behavior before actions are executed. It’s the difference between locking the barn door after the horses have escaped and having automated gates that only open for authorized animals.

Getting a Handle on the Evolution of Agentic AI in Financial Services
We’re at an inflection point for AI in the financial services industry. In our view Agentic AI systems are evolving from simple chatbots to autonomous digital workers that can:

• Process complex financial transactions independently
• Analyze risk patterns and make lending decisions
• Interact with regulatory reporting systems
• Coordinate across multiple banking platforms
• Communicate with other AI agents and external services

Each of these capabilities potentially introduces new safety or compliance risks and cost or carbon inefficiencies that traditional frameworks or tools weren’t designed to address. Agent-to-agent communication, dynamic tool access, and autonomous decision-making create entirely new categories of risk.

The financial services industry cannot afford AI systems that operate as black boxes. Every decision needs to be auditable. Every interaction must comply with regulatory frameworks. Every action requires real-time validation against both internal policies and external compliance requirements.

How to Get From Reactive Monitoring to Proactive Trust
Most current AI safety and efficiency approaches treat symptoms rather than causes. They monitor AI outputs after decisions have been made, flag suspicious activities in retrospect and generate alerts when it’s too late to prevent harm.

Runtime oversight flips this model. Instead of asking “what did our AI do wrong?” it asks “what should our AI be allowed to do right now?” This shift from reactive monitoring to proactive policy enables financial institutions to deploy AI systems that are simultaneously innovative and compliant.

Our work with leading financial institutions has shown that this approach delivers measurable business outcomes beyond security improvements. At NatWest Group, our proof-of-concept demonstrated significant operational AI cost reductions, improved response latency across banking operations and measurable carbon emission reductions certified under Green Software Foundation’s SCI ISO21031:2024 standards with third-party verification. The success created momentum for broader enterprise-scale adoption, giving NatWest confidence to expand AI innovation across multiple business units. Organizations report these kinds of significant operational improvements and sustainability benefits when AI systems operate within well-defined management frameworks from the moment they’re deployed.

What Being a Gartner Cool Vendor Means for Trustwise
We’ve been working to head off the potential risks inherent in AI deployment while keeping companies safe and efficient as AI learns, drifts and scales. The market is ready for a fresh approach to AI safety, efficiency, and compliance, one that addresses the dynamic nature of AI systems. Financial services organizations are moving beyond the question of whether to deploy AI and focusing concretely on how to deploy it safely, responsibly and at scale.

The banking industry has always been at the forefront of adopting new technologies while maintaining rigorous risk management standards. 

With the first autonomous AI Trust Management System, Trustwise addresses the gap between AI innovation and the rigorous risk management requirements where safety and oversight are non-negotiable, including financial services, healthcare, and industrial operations. Our flagship product, Harmony AI, and its modular shields embed “trust as code” into every layer of AI operations to ensure AI safety, efficiency, and security. The platform’s AI Control Tower acts as the HR Department for digital workers, providing real-time evaluation, optimization, and containment to ensure AI systems remain safe, efficient, and accountable at runtime. 

Looking ahead, the challenges in regulated industries will only intensify. AI systems will become more autonomous, more capable, and more integrated into business processes. Successful organizations will solve the trust equation early by deploying AI systems that deliver innovation benefits while building in safety, efficiency, and operational excellence.

Your AI systems passed the capability test. Now they need to pass the trust test.
Ready to transform your unpredictable AI agents into shielded, compliant digital workers? Learn more about how Trustwise can help your organization safely scale enterprise AI deployment.

Source: Gartner Research, Cool Vendors for Agentic AI in Banking and Investment Services, by  Jasleen Kaur Sindhu, Sudarshana Bhattacharya, September 2025

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