For years, cybersecurity has had a familiar villain: the external attacker. The hacker breaking through the firewall, stealing credentials or exploiting an unpatched vulnerability. It is the scenario we have trained for, built defenses around and spent decades trying to prevent.
Lead Analysts: Prabhakaran Ravichandhiran and Jeewan Singh Jalal
As a CISO advisor, I am observing a familiar pattern gaining a new, critical dimension. What we historically identified as "Shadow IT", the use of unapproved SaaS and tools, is rapidly evolving into "Shadow AI."
Employees are increasingly leveraging AI bots for drafting, analysis, code generation and strategic decision-making. While the intention is often to drive efficiency, the lack of governance creates a dangerous risk surface: sensitive data leakage, compliance violations and the potential for operational decisions based on unverified, AI-generated content.
When we first introduced Agent Risk Manager, the response was clear: many security teams are actively looking for a way to secure the AI agents already running in their environment and how they can confidently adopt AI across their organization.
AI agents have made their way into virtually every layer of your environment. They run in the apps your employees adopt, on the endpoints where agents execute code, as users with access privileges those agents borrow, and in the cloud workloads that scale them. The platform that you trust to secure your endpoints is already already covering where AI operates today.
Here is the through-line that makes this one problem instead of four. Every AI attack starts as an interaction and ends as an action. A prompt gets manipulated, an agent gets tricked, and the damage lands on a host, reaches into an identity, or moves through the cloud. The tools that treat each surface as a separate product hand you fragments. SentinelOne treats them as one chain.
Employee AI use is where the risk quietly enters. Your people are already using AI tools you never sanctioned, through browser, IDE, and API-connected apps and agentic AI tools. SentinelOne discovers that shadow AI use across browsers, IDEs, and copilots, highlights which tools and models are in play and governs it with policy. It keeps confidential data, PII, and secrets from reaching untrusted models, and it stops prompt injection and jailbreaks aimed at the tools you build. Legacy DLP reads patterns; this reads context, which is the only way to catch an attack aimed at AI systems that behave in a non-deterministic way.
The agent layer is where AI stops advising and starts acting. An employee’s prompt sends text. An agent sends commands, holds credentials, calls APIs, and chain actions without a human approving each step. That makes them non-human identities with standing access. SentinelOne governs that access. It inventories the agents and MCP servers already operating and scores what each one can reach and holds every agent to the privileges its task requires. Then it inspects the tool calls themselves, so an injected instruction gets blocked at the moment it would execute. What gets executed lands in a searchable record, and the same enforcement doubles as a kill switch.
While governance decides what an agent is allowed to do, the endpoint is where you find out what it actually did.
The endpoint is where agents execute. This is the frontier, and where SentinelOne has protected customers for over a decade. Our behavioral engine judges what a process does, not what it claims to be. That is how we caught QUIETVAULT – malware that spins up AI agents in “yolo” mode to exfiltrate secrets to GitHub. It is how we autonomously stopped the LiteLLM supply chain attack, where adversaries weaponized the Claude CLI to install a malicious payload. It is how we surfaced a DLL side-loading attack hidden inside an AI tool installer. Real detections, on the endpoint, today. Agents run on the host, and so do we.
The identity is where a hijacked agent runs next. Picture an employee’s AI coding agent that gets hijacked mid-task. It spawns a shell and reaches for cached credentials and cloud session tokens, trying to stop being a process and start being the user. That pivot to identity is what unlocks lateral movement, and it is where most AI attacks are headed. SentinelOne meets the move. It secures human and non-human identities alike, and seeds the environment with decoy credentials and honeytokens no legitimate user ever touches. The instant the hijacked agent grabs one, the trap trips, and Identity responds by forcing an MFA re-challenge, disabling the account, or isolating the host. Authorization at login is not enough. Access gets validated against behavior and pulled at runtime.
The cloud is where AI workloads scale. Consider an internal AI agent running in a Kubernetes cluster with standing access to a customer database. Security teams keep asking the same question about deployments like this. Where is the model connecting, and who is it talking to? SentinelOne answers with eBPF-native runtime protection that judges how the workload actually behaves, and flags the moment that inference service reaches an endpoint it has never touched before. It covers the control plane the deployment depends on, the secrets it reads, the pipelines it runs through, and the data it can access. Defending the AI you build takes more than watching it, it takes action on the workload in real time.
Each of these surfaces matters on its own. What closes the kill chain is following an attack across them without losing it at the handoff. This is where a single platform earns its keep. AI telemetry already streams into the Singularity Data Lake, alongside the endpoint data the platform has correlated for years. As identity and cloud signals join that same view, an analyst follows one attack from first prompt to final action, without stitching logs across six tools at two in the morning. A manipulated prompt, the process it spawns, the credential it reaches for, and the cloud resource it targets read as one story rather than six disconnected alerts.
Detection that only watches is observation. Runtime action is protection. When the Singularity Platform acts, autonomous response blocks the execution, rolls back the change, and revokes the access at the point of impact, without a human relaying orders between consoles. This is the difference between whether an attack is stopped or just gets logged.
That is the case for securing AI inside a platform built for autonomous runtime response. We are not adding a console to chase AI, we are extending the one already deployed where your agents run.
When evaluating AI security, ask yourself three things.
SentinelOne protects the surfaces where AI runs today. This includes the apps your employees use, the agents they deploy, the endpoints where agents execute, the identities they borrow, and the cloud where they scale. One platform, built for autonomous response. While AI has changed the attack, it does not have to change your architecture.
See it for yourself. Talk to our team about securing AI across your endpoints, identities, cloud, and the AI apps your employees already use, all from the platform you run today. Contact SentinelOne today.
Third-Party Trademark Disclaimer:
All third-party product names, logos, and brands mentioned in this publication are the property of their respective owners and are for identification purposes only. Use of these names, logos, and brands does not imply affiliation, endorsement, sponsorship, or association with the third-party.
Future-proofing organizations in the face of AI requires a unified defense strategy that secures both the human workforce and autonomous AI agents. One of the key requirements is shifting security cultures from reactive compliance to proactive, measurable behavioral change.
The recent developments surrounding vulnerabilities in major AI repositories like Hugging Face serve as a critical wake-up call for the cybersecurity community. As we accelerate toward an agentic future, the platforms we rely on for innovation are increasingly becoming the primary vectors for systemic risk.
Cybercrime used to have a ‘"tell." It was the digital equivalent of a villain stroking their cat - clunky grammar, misspelt links and suspicious attachments that screamed ‘phishing’.
So, you have some AI tools or are thinking about deploying them and want to know a bit about securing them.
You are not alone, but there are significant challenges due to the rapidly growing capabilities of AI, and the issues around new types of vulnerabilities we may not be used to thinking of. This is a very challenging area to attempt to secure, but I hope to point you in the right direction and set you up with some resources.
Boxers will often say, the punches that hurt the most aren’t the ones which are thrown with the most force, but the ones they didn’t see coming. I think the same is true in cybersecurity. It’s not the most advanced technically efficient, 0-day utilizing attacks that have the biggest impact, but rather those quiet ones. With no malware or suspicious login at three in the morning from an IP address in a country your company has never done business with. No alert fires. No dashboard turns red.
Login