Agentic AI is moving fast.
Really fast.
This is not a theoretical risk sitting somewhere in a Gartner slide deck.
Threat actors are already watching the AI gold rush, and some are actively targeting the organizations building, deploying, and relying on these systems.
One recent example is FulcrumSec, also known as “The Threat Thespians,” a cloud-focused extortion group that has publicly claimed victims across technology, professional services, financial services, healthcare, SaaS, and AI-related businesses. Public reporting has described FulcrumSec as an active threat actor focused on cloud environments, sensitive data theft, extortion, and leak-site pressure campaigns.
The message from Fulcrum Security is as follows:
“An emerging category of AI companies has positioned itself as the trusted custodian of humanity’s most intimate data, medical conversations, financial records, legal documents, private communications. They deploy language models that ingest and retain everything you share. They solicit access to your email, your passwords, your health histories, your children’s information, and the full contents of your professional life. They build their entire business model on the premise that you should trust them with all of it.
“Their security does not match their ambitions. In their rush to market, these snake oil salesmen are too busy polishing their pitch decks to be bothered with preventing leaks of private chats, recorded calls, and the confidential data entrusted to them. “Slopocalypse Now” is FulcrumSec’s exposure of the AI industry’s systematic failure to protect the data it collects.”
— Recited from Threat Actor’s Website Site: “Fulcrum Security”
Their “Slopocalypse Now” messaging is crude, theatrical, and intentionally provocative. But beneath the performance is a serious warning: AI companies and AI-adopting organizations are collecting, connecting, and centralizing enormous amounts of sensitive data, often faster than their security programs can mature.
That is exactly what makes agentic AI so attractive to attackers.
If an organization connects AI agents to email, documents, source code, financial systems, customer records, ticketing platforms, cloud environments, or internal knowledge bases, those agents may become high-value targets. Once compromised or manipulated, they can potentially help an attacker find, summarize, prioritize, and extract sensitive information far faster than a human intruder working manually.
In other words, the attackers are not waiting for enterprise AI to become perfectly secure. They are testing it now.
That is why the response cannot be, “We’ll secure it later.” Later is exactly the window threat actors are betting on.
Before organizations connect agentic AI to sensitive systems, they need to harden the environment around it, govern what it can access, and test how it could be abused.
Here are 10 things organizations should be doing today to reduce the risk of attackers using agentic AI against them:
1. Start Every AI Initiative With a Penetration Test
Agentic AI should never be treated like “just another software tool.”
Before deployment, organizations should know what the AI can access, what actions it can perform, who approved those permissions, and how its activity will be monitored. No agentic AI rollout should happen without cybersecurity at the table from the beginning.
2. Lock Down Identity and Access Controls
If attackers compromise identity, they may compromise the AI systems connected to it.
Organizations should enforce strong MFA, conditional access, role-based permissions, service account restrictions, and recurring access reviews. An AI agent should never receive broad access simply because it makes the tool easier to use.
3. Apply Least Privilege and MFA to Every AI Agent
Every AI agent should have only the access required to perform its specific job. Nothing more.
A customer service AI does not need access to financial records. A sales automation tool does not need access to legal files. An HR assistant does not need access to source code. Excessive permissions turn AI into a bigger blast radius.
4. Segment Sensitive Systems
If an AI agent can access everything, a compromised AI agent may become a shortcut to everything. Organizations should separate sensitive systems by function, risk, and business need.
Customer data, financial systems, executive communications, source code, internal documents, and privileged tools should not all live in one big digital soup pot.
5. Know Exactly What Data Your A.I. Can Access
Many organizations are adopting AI before they fully understand their own data exposure. That is dangerous.
Map what data the AI can access, including customer records, employee information, legal documents, financial records, healthcare data, passwords, secrets, source code, email, chat logs, and regulated information. If the AI can find it, an attacker may try to make it retrieve it.
6. Monitor AI Activity AND Your Users’ Prompts
AI activity should be logged, monitored, and reviewed like any other high-risk system activity. Organizations should be able to see what the AI accessed, when it accessed it, who triggered the action, and whether anything was exported, summarized, emailed, modified, or deleted. Unusual AI behavior should generate security alerts, not polite shrugs.
7. Test for Prompt Injection and AI Abuse Jailbreaking Scenarios
Attackers may try to manipulate AI systems through malicious prompts, poisoned documents, emails, tickets, web content, or hidden instructions. Organizations should test whether their AI can be tricked into revealing sensitive data, bypassing restrictions, trusting malicious instructions, accessing unauthorized systems, or executing unintended workflows.
Traditional vulnerability scans are not enough. AI needs adversarial testing.
8. Patch the Boring Stuff Before Buying the Exciting Stuff
Agentic AI does not replace cybersecurity fundamentals. It amplifies the consequences of ignoring them.
Unpatched systems, weak passwords, exposed services, stale accounts, poor logging, flat networks, and excessive permissions all become more dangerous when AI is connected to the environment. The boring stuff is still the load-bearing wall.
9. Update Incident Response Plans for AI-Enabled Attacks (Pulling the Plug)
Most incident response plans were not written for environments where AI agents can retrieve, process, and move information at machine speed. That needs to change.
Organizations should know how to disable AI access, revoke tokens, isolate integrations, preserve logs, and determine what data may have been exposed. When AI is involved, response time matters even more.
10. Get an Independent Penetration Test Before Roll-Out to Users
The most dangerous assumption is, “We’re probably fine.” Agentic AI changes the risk equation.
Before connecting AI to sensitive systems, organizations should bring in an outside security team to assess the environment, test assumptions, identify weaknesses, and prioritize fixes. The penetration test should evaluate both the AI implementation and the cybersecurity foundation underneath it.
Because an AI agent connected to an insecure environment does not simply inherit risk. It can multiply it.
Final Thought: AI Should Accelerate the Business, Not the Breach
Agentic AI has enormous potential. But potential cuts both ways.
The same capabilities that make AI useful to employees can also make it useful to attackers: fast search, fast summaries, fast automation, fast access, and fast decisions.
If the environment is secure, governed, monitored, and tested, AI can be a force multiplier for productivity. If the environment is weak, over-permissioned, unmonitored, and poorly understood, AI can become a force multiplier for compromise.
Every AI initiative should begin with a cybersecurity initiative.
Before organizations ask:
“What can AI do for us?”
They should ask:
“What could an attacker make our AI do to us?”
That question may be the difference between innovation and Slopocalypse Now.