Traditional software does exactly what you tell it to. An AI agent does what it understands it should do — and then autonomously selects the tools, data, and steps to get there. This autonomy makes it a fundamentally different security problem. It’s not just another endpoint you can protect with a firewall. It’s an entity that moves inside your infrastructure, has access to tools and data, and makes decisions in a non-deterministic way.
In this article, we’ll look at the numbers, real-world risks, and concrete steps every enterprise should consider in the first half of 2026.
Why Agentic AI Is a Different Kind of Risk¶
The traditional security model assumes that software is predictable. You know which APIs it calls, what data it reads, where it writes. You can test it, certify it, and deploy it with confidence that it will behave the same way as in testing.
AI agents break this premise. They have three properties that make them a unique security challenge:
- Autonomous decision-making — the agent chooses its own approach, tools, and sequence of steps. Two identical requests can lead to different execution paths.
- Tool use — agents call APIs, read databases, execute code, send emails. Every tool is a potential attack surface.
- Non-deterministic behavior — output depends on context, prompt, and the current state of the model. Traditional unit tests aren’t enough.
IBM’s research compares AI agents to “digital insiders” — they have employee-level access but lack human judgment, loyalty, and the ability to recognize social manipulation. McKinsey goes further, recommending that organizations treat AI agents like insider threats — applying the same threat model as for a potentially compromised employee.
Numbers and Trends: Where the Market Is Heading¶
Agentic AI adoption is massive and accelerating:
- 79% of organizations are already deploying AI agents in some form (PwC 2026 AI Business Predictions).
- 88% of executives plan to increase AI budgets in 2026 (PwC).
- AI agents are no longer experiments — they’re becoming part of core business processes from customer support to financial analysis to DevOps automation.
At the same time, awareness of risks is growing:
- 48% of security professionals identify agentic AI as the top attack vector for 2026 (DarkReading / Gartner survey). This is a significant shift — just a year ago, concerns were dominated by ransomware and supply chain attacks.
- NIST, through its NCCoE (National Cybersecurity Center of Excellence), has published a 2026 roadmap where AI security stands as one of four strategic pillars alongside post-quantum cryptography, zero-trust architecture, and software supply chain security.
In other words: enterprises are deploying agents faster than they can secure them. And that’s exactly the gap attackers will exploit.
Key Security Risks¶
Tool Poisoning¶
Agents work with tools — APIs, databases, file systems. An attacker doesn’t need to compromise the model itself. They just need to poison a tool the agent uses. Imagine an agent that reads data from an external API. If an attacker modifies the API’s responses, the agent will take actions based on manipulated data — actions it would never have taken otherwise.
Tool poisoning is insidious because agents trust their tools implicitly — they have no reason to doubt data from a configured source.
Prompt Injection¶
The most well-known attack vector for LLM systems, but with far more serious consequences for agents. While prompt injection in a chatbot leads to an unwanted response, in an agent it leads to an unwanted action. An injected prompt can make an agent:
- Send sensitive data to an external endpoint
- Modify system configuration
- Escalate its own privileges
- Ignore security guardrails
Indirect prompt injection — where the malicious prompt is hidden in data the agent processes (an email, document, or web page) — is especially dangerous because no perimeter firewall will catch it.
Privilege Escalation¶
Agents need permissions to do their jobs. The problem arises when they gain more permissions than they need — whether through poor design or active exploitation. A typical scenario: an agent has access to a read-only API for customer data. An attacker, via prompt injection, gets the agent to use a different endpoint on the same API — this time for writing or deleting.
In traditional systems, we address privilege escalation through RBAC and least privilege. With agents, it’s more complex because the scope of actions an agent needs changes dynamically based on context.
Data Exfiltration¶
An agent has access to data and the ability to communicate with external services. This combination makes data exfiltration trivial — if proper controls are missing. An agent can be instructed (directly or via injection) to:
- Embed sensitive data in its output, which is accessible to a third party
- Call a webhook with internal data as payload
- Write data to a shared storage accessible to the attacker
Unlike an employee who realizes “I probably shouldn’t be sending this out,” an agent has no such judgment.
Best Practices: How to Secure AI Agents¶
Sandboxing¶
Every agent should run in an isolated environment with clearly defined boundaries. This means:
- Network isolation — the agent doesn’t have access to the entire network, only to explicitly allowed endpoints.
- File system isolation — the agent only sees directories it needs.
- Process isolation — containerization (Docker, gVisor) or VM-level isolation for critical workloads.
Sandboxing isn’t just about preventing attacks. It’s a safety net against mistakes — and agents make mistakes frequently because they’re non-deterministic.
Human-in-the-Loop (HITL)¶
Not every agent action needs human approval — that would defeat the purpose of automation. But critical actions do:
- Operations with financial impact (payments, invoice approvals)
- Access to sensitive data (PII, health records, trade secrets)
- Infrastructure configuration changes
- Communication with external parties
Implement tiered approval — the agent can perform routine tasks autonomously but escalates to a human when it exceeds a defined risk threshold.
Least Privilege¶
The principle of least privilege isn’t new, but agents require a specific approach:
- Dynamic permissions — the agent gets access to a tool only for the duration of a specific task, not permanently.
- Scoped tokens — instead of one API key with full access, use tokens limited to specific operations and resources.
- Explicit allow-lists — define what the agent may do, not what it may not. Deny-by-default.
Monitoring and Audit Trail¶
Every agent action must be logged with sufficient context for forensic analysis:
- What prompt the agent received
- What tools it called and with what parameters
- What data it read and where it wrote
- How it decided and why (chain-of-thought logging)
Monitor for anomalies: unusual API call patterns, data access outside normal scope, attempts to communicate with unauthorized endpoints. Behavioral monitoring is key — as IBM emphasizes, threat modeling for AI agents must be behavioral, not just technological.
Zero-Trust for Agents¶
Zero-trust architecture isn’t just for human users. Apply it to AI agents as well:
- Never trust, always verify — every agent request is individually authenticated and authorized.
- Microsegmentation — the agent has access only to the infrastructure microsegment it needs for the current task.
- Continuous verification — permissions are continuously verified, not just once at agent startup.
- Assume breach — design systems assuming the agent could be compromised.
Regulatory Landscape: EU AI Act and NCCoE¶
EU AI Act — Article 14: Human Oversight¶
The EU AI Act, fully effective since February 2025, explicitly requires human oversight for high-risk AI systems in Article 14. This includes:
- The ability for humans to understand the AI system’s outputs
- The option to ignore, override, or stop AI decisions
- The ability to intervene in real time (stop button)
For agentic AI, the implications are direct: if your agent processes data in a regulated industry (finance, healthcare, HR), you must implement HITL mechanisms that meet Article 14 requirements. Full autonomy without human oversight is non-compliant in these cases.
NCCoE (NIST) 2026 Roadmap¶
The US National Cybersecurity Center of Excellence under NIST has published its strategic plan for 2026, where AI security stands as one of four pillars:
- AI & Machine Learning Security — security of AI systems including agentic architectures
- Post-Quantum Cryptography
- Zero-Trust Architecture
- Software Supply Chain Security
NCCoE is working on practical reference architectures and guidelines that will be directly applicable to enterprise AI agent deployments. For companies operating globally or supplying to the US market, this is a relevant regulatory signal.
Our Recommendations: The CORE SYSTEMS Perspective¶
Based on our experience with enterprise clients, we see a clear pattern: organizations that address AI agent security proactively have a significantly lower risk profile than those that react only after an incident. Here are our recommendations:
1. Conduct an AI Agent Threat Assessment¶
Before deploying another agent, map out: - What agents you have in production (including shadow AI) - What data and systems they can access - Who owns them (accountability) - What the worst-case scenario is if they’re compromised
2. Implement an Agent Governance Framework¶
Define rules for the entire AI agent lifecycle: - Approval — who can deploy a new agent and under what conditions - Permissions — how they’re granted, reviewed, and revoked - Monitoring — what gets logged and who reviews it - Incident response — what happens when an agent goes rogue
3. Start with Sandboxing and Least Privilege¶
You don’t have to implement everything at once. Two steps with the highest ROI: - Isolate agents in sandboxes (containers, network segments) - Limit permissions to the minimum needed for the use case
4. Deploy Behavioral Monitoring¶
Traditional WAFs and IDS can’t see what an agent does inside your infrastructure. You need: - Logging of all tool calls and agent decisions - Anomaly detection on behavioral patterns - Alerting on unusual data access patterns
5. Prepare for Regulation¶
The EU AI Act and NCCoE roadmap clearly indicate the direction. Prepare: - Documentation of HITL mechanisms - Audit trails for all high-risk AI operations - A compliance plan for EU AI Act Article 14
6. Adopt a Zero-Trust Mindset for AI¶
Stop thinking of the agent as a trusted tool. It’s an entity with access — and every entity with access must be continuously verified, monitored, and constrained.
Conclusion¶
Agentic AI is a transformative technology. But transformative technologies bring transformative risks. 2026 is an inflection point — most enterprises already have agents or are deploying them, but only a fraction have them properly secured.
The numbers are clear: 48% of security professionals see agentic AI as the top attack vector, 79% of organizations are already deploying agents, and regulators are tightening the rules. The window for proactive preparation is closing.
AI agent security isn’t a technical nice-to-have. It’s a business-critical priority. And the sooner you start addressing it, the less it will cost — in money and in reputation.
Need help with a security audit of AI agents in your organization? Contact us — we’ll help you identify risks and design a tailored solution.
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