Agentic AI vs Generative AI in Cybersecurity: What MSPs Need to Know

Summary

Generative AI creates content when prompted. Agentic AI works towards goals it has been given: planning steps, using tools, and taking action with less human input. Businesses already use AI across daily operations, and attackers are using agents too. Documented cases show AI running most of an intrusion end to end. At that pace, the time required to contain threats is critical for controlling the outcome, so human-governed agentic AI is becoming the baseline for Microsoft 365 security.

Versioned Specifics:

• Original publication date: 28 October 2026
• Key problems addressed: understanding agentic AI vs generative AI, AI-enabled attacks on Microsoft 365, response speed, human oversight of security automation
• Relevant inforcer solutions: inforcer TDR, 365 Manager

Agentic AI vs generative AI in cybersecurity: what MSPs need to know

Most people have used generative AI. You ask it for something and it writes, summarizes or draws the result.

Agentic AI is different: once you give it a goal, it plans the steps, uses whatever tools it has access to, and keeps going until the job is done, with little input from you along the way.

The difference has critical implications within cybersecurity. Many threat actors are already using agents to attack at previously impossible speeds, so MSPs responsible for defending their tenants need a way to respond at the same speed.

Agentic AI is also a key part of how inforcer TDR correlates alert signals across every Microsoft 365 environment your MSP manages to screen out false positives and contain genuine threats within custom rules.

This guide explains the difference between both kinds of AI, along with why more businesses are moving to trust AI agents with real work, what kind of damage AI-driven attacks are already doing, and why the speed of your response is now at the heart of the value your MSP offers customers.

Generative vs. Agentic AI Explained Clearly

What generative AI does

Generative AI produces content in response to a prompt. Ask for a customer email, a meeting summary or a block of code, and it generates one in response.

IBM's definition is one of the clearest: generative AI creates content from a prompt, and a person decides what to do with the result.

This kind of technology is already useful in security operations. Generative AI can explain an alert in plain English, summarize an incident, or draft the report a customer will read. Every output is reviewed by a person before anything happens, which makes it safe by design.

But it is also slow by design. A tool that needs to wait for humans to prompt it before it can perform any action is limited by the speed of that oversight.

What agentic AI does

Agentic AI starts from a goal rather than a prompt. Microsoft describes the loop as perceive, reason, plan, act and learn. The agent:

• Observes its environment
• Works out the steps needed to reach the goal
• Calls the tools it has been given
• Takes action
• Adjusts based on the result until it determines that the goal has been achieved

With generative AI, people prompt and review. With agentic AI, people set the goal and the guardrails, then assess the result once the AI delivers it.

That doesn't make agentic AI a separate technology. Most agentic systems run on the same large language models (LLMs) that power generative AI tools. What changes is the structure around the model: instead of stopping after one response, the system runs it in a loop (a pattern often called ReAct, short for reasoning and acting) where the model plans a step, calls a tool, reads the result and decides what to do next, repeating that sequence until the goal is met.

The difference lies in the tools the system has been given, the authority to use them, and the instruction to keep going.

Generative vs. Agentic AI at a Glance

The practical difference is what each type of AI is allowed to do:

Sources:

• Agentic AI vs generative AI (IBM)
• What is agentic AI in cybersecurity? (Microsoft Security)
• Agentic AI (NIST)

Because the same models sit underneath both, confusion about what distinguishes these systems is common. Gartner has warned that more than 40% of agentic AI projects will be cancelled by the end of 2027, mostly because of unclear value and weak risk controls, and uses the term "agent washing" for existing products, such as chat assistants and automation tools, that are rebranded as agents without gaining agentic capabilities.

In practice, the last two rows of the table are the test. A system that can only respond to prompts is an assistant, however capable the model behind it. A system that follows fixed if-then rules is conventional automation.

An agent combines the two: a model that reasons about a goal, the tools and authority to act on it, boundaries set by a person, and a log of every action it took.

Businesses already trust agents with real work

The agent model is already moving from pilot to practice. Microsoft's 2025 Work Trend Index found that 46% of leaders say their companies are using agents to fully automate workflows or processes, and 81% expect agents to be moderately or extensively integrated into their company's AI strategy within the next 12 to 18 months.

McKinsey's 2026 State of AI survey shows how early that shift still is: about two in ten organizations have reached the scaling phase with AI agents, and among large companies, 40% are scaling agents in at least one business function. Adoption is uneven, but the pattern is set: give an agent a defined job with clear limits, let it run, and review the results.

Security is the obvious next place for that pattern, and Microsoft has already moved in that direction. Security Copilot agents can triage phishing in Defender, optimize Conditional Access policies in Entra, and recommend vulnerability remediation in Intune. These are examples of agentic products every MSP managing Microsoft 365 can already access.

So the question is no longer whether agents belong in security operations. It is how effectively people can use them, and what they’re using them for.

Because the other side of the story is that not everyone using agentic AI is doing so with noble intentions.

Attackers are already using AI agents

Our CEO Jamie Daum put it plainly in July 2026: "Anthropic's Claude Code can make anyone a 'vibe coder' and Mythos has the potential to make anyone a hacker." By now, there is ample evidence to back this claim up.

In fact, the trend began even earlier: back in November 2025, Anthropic disclosed a state-sponsored espionage campaign in which an AI agent performed an estimated 80 to 90% of the tactical work: reconnaissance, exploit validation, credential harvesting, lateral movement and exfiltration staging, at a rate of multiple operations per second. Humans sat at a handful of decision points.

In July 2026, Sysdig reported JadePuffer, the first ransomware intrusion run end to end by an LLM agent, which recovered from a failed login in 31 seconds and moved on. Microsoft's 2026 Digital Defense Report says AI now compresses credential discovery, lateral movement and exfiltration from days to minutes, and expects agentic attacks to become commodity tooling within a year.

These are early cases, and are not yet the norm. Mandiant's M-Trends 2026 is clear that most 2025 intrusions still traced back to human and systemic failures, and the Anthropic campaign had real limits: only a handful of targets were compromised, and the agent fabricated credentials along the way.

And attackers were already fast before AI. CrowdStrike's 2026 Global Threat Report puts the average eCrime breakout time, from initial access to lateral movement, at 29 minutes, with the fastest observed at 27 seconds. So the long, patient intrusions of a decade ago have been the exception rather than the rule for quite some time.

But agentic AI has the potential to accelerate this even further by removing the remaining human bottleneck. Now, an attacker no longer needs to be skilled, attentive, or even awake for their attack to progress.

Containment must now move at the speed of AI

If an attacker can go from phishing email to mailbox rule to data access in the time it takes to manually triage a single alert, the question is no longer whether you can detect the attack. The question becomes how long it takes you to stop it.

Manual response has a floor. Across the MSPs we work with, triaging a single alert by hand usually takes 15 to 30 minutes, and a typical week can produce 100 to 200 of them.

This means an engineer who picks up an impossible travel alert at 9:15 and confirms the compromise at 9:40 is moving at a respectable pace by manual standards, but will still lose to an attacker who used an AI agent to move from access to lateral movement in minutes. And an engineer who has to do this hundreds of times a week will burn out quickly.

The cost of that gap is well documented. IBM's 2026 Cost of a Data Breach report puts the average breach at $4.99M, a record, and found that AI-driven attacks rose 56% year on year. It also found that organizations using security AI and automation extensively saved an average of $1.93M per breach compared with those using none. Speed on the defending side is what makes the difference, and Microsoft's own conclusion in the 2026 Digital Defense Report is that defenders need people working with agentic systems, because people alone can no longer keep up.

Today, effective security means using AI to fight AI. An agent that reads every signal as it arrives, correlates it against everything else happening in the tenant, and acts the moment it confirms a genuine threat is the only way to meet attackers who use the same kinds of tools.

Related: How the Modern SOC Is Evolving

What a human-governed AI security agent looks like

None of this means handing the keys to an AI and walking away. It means the opposite: the agent handles speed and volume, and people decide what it is allowed to do.

Microsoft's guidance for agentic security describes the shape of that arrangement, and also aligns with how we designed our own right of boom security solution: inforcer TDR:

• People set the goals and the boundaries. Security experts train the system and decide what counts as a threat. Agents then run containment procedures within the rules of engagement set by the MSP, which can be unique for each tenant.
• The agent acts within those boundaries, at machine speed. High-risk threats are contained automatically, revoking sessions and locking accounts, with an average containment time under 60 seconds.
• High-impact actions stay with people. Password resets and re-enabling a user are left to the engineer, because each new credential has to be handed over securely.
• Everything is logged and ticketed. Every action is recorded with detailed notes so that the MSP engineers can follow the story of a breach rather than waste time working backwards from raw alerts. This also makes it easier to provide the customer with a report of what happened and what was done about it.
• People stay reachable. Partners can talk to inforcer’s specialist team if further triage is needed (although by then, the agent has usually already completed the containment process).

That combination is what makes the right agentic AI solutions trustworthy in security operations: not the ability to act unsupervised, but the ability to act within limits people set, faster than people can move on their own.

Learn More: Multi-Tenant Threat Detection for MSPs in Action

Can you respond as fast as your attackers move?

Generative AI will keep making security teams more productive by drafting the reports and explaining the alerts. But agentic AI is what truly changes the balance of power, because it enables the only kind of response that can move at the same speed as modern attacks.

The deciding questions for any MSP are simple: how long does it take to contain a compromised Microsoft 365 account today, and how much effort is required to make sure it happens?

See how inforcer TDR can contain threats to your Microsoft 365 tenants in under 60 seconds:

Scope & Limitation Statement

This guide explains the difference between generative and agentic AI as it applies to Microsoft 365 security for MSPs, drawing on definitions from IBM, Microsoft and NIST and on published threat research as of October 2026. It is an introduction, not a technical reference, and it doesn't cover how to build or govern AI agents in your own environment. The AI-driven attacks described are documented cases, not yet typical of most intrusions. Third-party statistics come from different populations and methodologies, and the attack timeline graphic is illustrative. Descriptions of inforcer TDR reflect the product as of October 2026.

FAQs

What is the difference between agentic AI and generative AI?

Generative AI creates content in response to a prompt, and a person decides what to do with the output. Agentic AI usually runs on the same models, but wraps them in a loop that plans steps, uses tools and takes action toward a goal with limited human input. Generative AI answers; agentic AI acts.

Is agentic AI safe to use in cybersecurity?

Yes, when it operates within boundaries people set. A well-governed security agent has defined rules of engagement, approval gates for high-impact actions such as password resets, least-privilege access, and a full log of every action taken. Microsoft's own guidance for agentic security follows the same model: automation handles speed, people keep control of what it is allowed to do.

Are attackers really using AI agents?

Yes, in documented cases. Anthropic disclosed a 2025 espionage campaign in which an AI agent performed most of the intrusion work, and Sysdig reported ransomware run end to end by an LLM agent in 2026. Microsoft expects agentic attacks to become commodity tooling within a year. Most attacks still involve humans, but the trend is clear.

Why does containment speed matter more than detection?

Because attackers now move from initial access to lateral movement in an average of 29 minutes, according to CrowdStrike's 2026 Global Threat Report. Detecting an attack an hour later is too late to prevent damage. Containing it in seconds, by revoking sessions and locking the account, stops the attacker before data is accessed.

How does inforcer TDR use agentic AI?

inforcer TDR uses AI to correlate signals from every layer of Microsoft 365 within each tenant, confirm genuine threats and contain them automatically, revoking sessions and locking accounts within rules of engagement the MSP sets. Average containment time is under 60 seconds. Every action raises a PSA ticket, and our specialist team trains the detections.