Autonomous AI Agents Are the Next Insider Threat: What Leaders Should Know

Security operations (SecOps) teams have learned to defend against outsiders. However, the next threat won’t come from the outside at all. It will log in with valid credentials and act like a trusted user.

SecOps spent years getting really good at spotting the usual suspects: compromised users, stolen credentials, malware on endpoints and suspicious automation. The playbook is familiar.

• Watch for logins that don’t make sense
• Flag privilege escalation
• Correlate odd behavior
• Contain quickly

But a new class of actor is quietly entering enterprise environments, and it doesn’t fit any of those traditional models. They’re called autonomous AI agents. Tools like OpenClaw don’t just answer questions, they execute commands, move files, call APIs and interact with systems using real credentials and real permissions. They don’t assist humans; they act on their behalf. The issue isn’t that it’s malicious. It’s that it’s powerful, autonomous and high privilege by design, making them risky without tight governance.

Personal AI agents like OpenClaw are groundbreaking from a capability perspective but present significant security risks, Cisco experts wrote in their recent article, “Personal AI Agents like OpenClaw are a security nightmare.” “Granting an AI agent unlimited access to your data (even locally) is a recipe for disaster if any configurations are misused or compromised,” the experts said.

And that subtle difference changes everything for SecOps. The moment software starts taking independent action inside your environment, it stops being a tool and starts behaving like an identity. From a SecOps perspective, every new identity expands your attack surface. The challenge is scale. Most identity programs were designed around managing human users and a predictable number of service accounts.

AI agents fundamentally change that equation. Instead of provisioning one automation account per system, organizations may deploy tens or hundreds of micro-agents, each with narrowly scoped permissions and independent workflows. Without lifecycle management, visibility and behavior monitoring, these agents can quickly outnumber traditional privileged accounts, dramatically increasing the potential attack surface without triggering traditional identity risk indicators.

The impact is not theoretical. The number of autonomous agents operating inside enterprise environments is already growing at a pace that most governance programs were never designed to support.

What makes this shift especially significant is the speed at which autonomous agents are being deployed. Industry analysts at Gartner estimate that “AI agents will be implemented in 60% of all IT operations tools by 2028, which is an increase from fewer than 5% at the end of 2024.”

In early enterprise pilots, it is not uncommon to find dozens to hundreds of task-specific AI agents operating across IT, development, customer service, finance and security operations, each interacting with systems using API credentials, service accounts or delegated access. Individually these agents may appear low risk, but collectively they represent a rapidly expanding population of non-human identities that must be governed, monitored and secured.

What makes AI agents tricky isn’t that they look malicious. It’s that they look completely normal. They log in with valid credentials. They operate from trusted devices. They perform legitimate tasks. They often run inside sanctioned workflows. If something goes wrong, the telemetry doesn’t scream “attack.” It looks like business as usual.

This visibility challenge mirrors the early evolution of cloud adoption, where shadow IT expanded faster than governance controls. AI agents are beginning to create a similar phenomenon – “shadow automation.”

Business units are increasingly deploying embedded AI assistants within SaaS platforms, low-code environments and developer pipelines without centralized security onboarding. As a result, security teams often discover these agents only after they begin interacting with production systems, creating blind spots in logging, identity governance and behavioral monitoring.

Most security operations center (SOC) controls are designed to detect unauthorized access. But AI agents live in the gray area of authorized misuse. They can move quickly, chain together multiple actions, and operate continuously without fatigue or oversight. If compromised, misconfigured or manipulated through prompt injections or malicious extensions, they can cause damage faster than any human insider ever could.

That’s exactly the problem. This isn’t a malware problem. It’s an identity problem.

The mental model shift is simple but important: stop treating AI agents like software and start treating them like users. Operationally, they behave the same way. They authenticate, make decisions, touch sensitive data and execute changes across systems. That puts them squarely into insider-risk territory.

“Insiders aren’t just people anymore. They’re AI agents logging in with valid credentials, spoofing trusted voices, and making moves at machine speed. The question isn’t just who has access —  it’s whether you can spot when that access is being abused,” said Steve Wilson, chief AI and product officer at Exabeam, in a research announcement, published via Business Wire.

That line hits home for any SOC leader. Most programs are excellent at blocking outsiders. Fewer are designed to detect legitimate credentials being used in abnormal ways. AI agents amplify that exact blind spot.

Historically, we’ve relied on static guardrails, hard rules, allow lists and known patterns. But autonomous agents don’t behave in deterministic ways. Their actions change based on inputs, context and learning. What they do today might not match what they did yesterday.

Traditional rule-based controls were designed for predictable system behavior, but AI agents introduce variability by design. Because agents adapt their actions based on context, prompts and changing datasets, their activity patterns naturally evolve over time.

This means static allow-lists and fixed automation rules cannot reliably define “safe” behavior. Instead, organizations must shift toward continuous behavioral baselining, where expected activity is defined dynamically and risk is measured based on deviations rather than predefined signatures.

That makes brittle controls less effective. Simply restricting access isn’t enough. Simply trusting automation isn’t safe. You need visibility into behavior.

“Securing the use of AI and AI agent behavior requires more than brittle guardrails; it requires understanding what normal behavior looks like for agents and having the ability to detect risky deviations,” said Steve Wilson in another article featured on Business Wire.

That’s not an AI research challenge. That’s classic SecOps. It’s the same evolution we went through with users decades ago: moving beyond signatures and into behavior analytics.

• Establish baselines
• Look for anomalies
• Detect misuse instead of just unauthorized access

Now we have to do the same for non-human actors.

As Walt Powell, lead field CISO at CDW, notes, SecOps teams must strike a balance between enabling AI productivity and managing its security implications: “We have to take methodical approaches while not slowing down innovation for leveraging AI for your business.”

Blocking AI adoption isn’t’ realistic. These agents are coming whether we like it or not, driven by productivity gains across the business. For SecOps teams, the path forward is to operationalize them safely.

That means treating every agent as a managed identity with clear ownership and least-privilege access.

• Logging their actions with the same fidelity as endpoints
• Baselining behavior
• Alerting on anomalies
• Keeping humans in the loop for high-risk decisions

In other words, the same fundamentals we already apply to users — just extended to machines that think and act.

The emergence of autonomous agents marks a broader transition from human-centric identity security to hybrid identity ecosystems, where human users, service accounts, robotic process automation and AI agents all operate side by side. Security operations programs that fail to treat these identities consistently risk creating fragmented detection models in which human activity is monitored rigorously while non-human actors operate with minimal security.

Over the next several years, the number of autonomous agents operating inside enterprise environments is expected to grow exponentially as agentic AI capabilities are embedded into productivity platforms, developer tools and enterprise applications.

Security teams that treat this trend as a niche automation issue risk falling behind the adoption curve. Those that proactively adapt identity governance, behavioral analytics and detection engineering practices will be better prepared to manage the next generation of non-human operational risk.

Autonomous AI isn’t just another tool category you can bolt onto the stack. It introduces a new class of actor inside your environment: one that works 24/7, moves at machine speed and can quietly operate with trusted credentials.

If you don’t explicitly manage and monitor these agents, they become the perfect blind spot. Helpful automation today can easily become tomorrow’s breach vector. The organizations that succeed won’t avoid AI. They’ll govern it like identity.

Because in the SOC of the future, insider threat won’t just mean people.