2026 Will See AI Agents Explode Across Businesses: Are We Prepared for the Security Risks?

Enterprises are racing to adopt AI agents, but they lack the infrastructure to do so safely. Agents are different from AI tools: they work autonomously, access data continuously, and act at machine speed without need for supervision. That creates completely new risks that we must prepare for.

People are already using their own AI agents at work – without necessarily telling their bosses. How do we make sure that OpenClaw bots aren’t deleting important files? Three in four large enterprises will have adopted multi-agent systems by the end of 2026, according to Gartner. How do we make sure that each of those agents doesn’t open up doors for hackers to attack us in novel ways?

Current security infrastructure is made to address known, human-paced threats. That doesn’t account for rogue agents tampering with enterprise code without us knowing. Or for bad actors attacking us via prompt injections: instructions hidden in code or prompts that cause the AI to, for example, expose sensitive information or execute malicious code.

Truth is, companies are more hungry than they are prepared for this coming wave. Just 29% of organizations “strongly agree” that they have safe AI protections in place, according to a Microsoft survey.

Company leaders need to do several things.

1. Implement AI-native security measures company wide.
2. Treat AI agents with the same security measures as humans.
3. Increase visibility of what these agents are doing.

By executing these controls, security can match the speed of agents and enable innovation rather than slow it down.

Create AI-native security

In February, a Meta security researcher made a surprising post on X: her OpenClaw bot had started to delete her emails and ignored her orders to stop. By the time she was able to interrupt the process manually, the damage had been done.

This is an illustration of the incredible speed at which rogue AI can act, making human-dependent controls insufficient. Security needs to move as fast as – and along the same pathways as – agentic AI.

This requires renewing a company’s security infrastructure, not simple upgrades to existing tooling. Several areas need to be involved in this system-wide shift in tooling.

Avoiding fast-paced situations like that of the Meta researcher requires AI tools that detect anomalies in real time and respond automatically. For example, supervisor agents can be deployed to review high-impact tasks executed by other agents. Agentic Threat Intelligence (ATI) tools can detect and act automatically in the early stages of a potential attack.

Another important prevention mechanism is to ensure the safety of the actual model being deployed on your systems. Platforms like ProtectAI, TrojAI, or Aim Security can test for direct dangers like prompt injection, using AI tools to cleanse code before publishing.

Securing the provenance of the data that AI agents are exposed to is also crucial. Tools like Databricks Unity Catalog, Collibra, or Alation enable teams to govern data movement.

We can also prevent tampering with our AI agents by securing the physical devices we use to deploy them. Platforms like CrowdStrike can help avoid the misuse of AI credentials and detect abnormal behavior.

Having principles that govern your internal operations as well as how you select your vendors is essential. Use existing industry frameworks for guidance, like Google SAIF to build a secure architecture, AWS CAF-AI for AI governance, or NIST AI RMF for compliance. The OWASP Gen AI Security Project – an open-source initiative for mitigating the risks of AI – offers relevant security guidance for enterprises deploying AI.

Treat AI with the same limitations as humans

Last year, an investor was using a Replit AI agent to vibe code – until on day nine, the agent decided to delete code from a live production database, affecting 1,200 companies.

Because agents can act autonomously, granting them unmonitored access to a broad array of documents, data and APIs can be disastrous. One reason is that businesses often don’t assign them an “identity” in the same way we would a human user, meaning they can’t track, monitor and manage their access to different spaces.

Businesses can act by assigning identities to non-human users: identity access management providers can closely manage which areas agents have access to, for how long, and pinpoint any risky behavior. This reduces over-permissive access to areas the agent doesn’t need to be. It also limits what it can do in the areas where it operates.

Assigning agent identities also reduces the threat from external actors.

When a team of researchers injected malicious prompts into an AI application that manages legal contracts, one user was able to retrieve private data belonging to a separate user. Clearly there was no secure separation of user-level data, and authorization to such data was granted to the AI even when it wasn’t relevant to the scenario at hand.

Establishing boundaries for AI “identities” means they won’t have broad “always-on” access. For example, agents will require authorization for certain functions, or be granted read-only capabilities in certain areas. By doing so, even if an AI agent is hacked or manipulated by a bad actor, the impact remains limited rather than catastrophic.

Leadership should also educate their teams on the threat of agents operating clandestinely. The risk is greater when employees connect “shadow” AI agents to workspaces without leadership actually knowing and providing proper identification and safeguards. Furthermore, a Zero Standing Privileges (ZSP) approach creates a baseline of 0 access for any agent. So if an employee connects an unsanctioned shadow agent, it enters an environment in which all the doors are shut.

Have eyes on your agent

While we know the “AI is a black box” analogy, its deployment in your business should very much be observed and monitored.

In order to enforce strict security policies, every agent must be governed: they should have clearly documented ownership, responsibilities and controls so you can accurately track its pathways. You should have continuous telemetry – remote monitoring – tracking agent behavior, and feeding your existing security tools so they can detect anomalies and execute adaptive trust scoring.

Leadership needs to take this new security frontier seriously and make sure different teams are accountable for their use of AI agents. They can create new compliance KPIs, such as “Number of datasets with verified provenance,” “% of AI agents with managed identities,” or “AI Trust Score.”

This is critical to prevention, but it’s also about being auditable – even non-human IDs need to have an audit trail for an organization to be compliant.

We generally understand how humans behave and what pathways they take within workspaces. But AI is a completely new beast. There’s a lot we don’t know about how it operates. Some companies are choosing the short-term solution of blocking AI from the office – but that will merely delay the issue or cause employees to use shadow AI. Prepare your security architecture proactively for agents that are already becoming ubiquitous.