“World models” are suddenly everywhere.
In robotics, they’re used to predict physical outcomes.
In reinforcement learning, they enable planning and simulation.
In frontier AI, they’re described as the missing ingredient for agents that can act coherently over time.
Plus - if both Fei-Fei Lee and Yann Le Cun are into them - they must be important.
Strip away the hype and a world model is something much simpler — and much more useful:
A learned internal representation of how a system evolves, such that future states can be anticipated from past states.
By that definition, cybersecurity has always needed world models.
We just didn’t have the tools to build them.
The World That Matters in Cybersecurity
Cybersecurity is sometimes described as a “network problem,” but that framing is misleading.
We are not defending networks.
We are defending systems of humans and machines.
- Employees authenticating and accessing resources
- Services calling other services
- Automated processes behaving as expected — or not
- Adversaries probing, adapting, escalating, and hiding
The network is not the world.
It is however a place where footprints are left — reliably, continuously, and at scale.
Those footprints are valuable not because they describe infrastructure, but because they reflect behavior.
Why Rules and Reasoning Aren’t Enough
Traditional security systems attempt to reason about this world using:
- Rules
- Signatures
- Thresholds
- Point-in-time correlations
More recently, agentic approaches propose assembling context at decision time — retrieving logs, alerts, and summaries, then asking a reasoning model to decide what’s happening.
This works for some problems.
It fails when:
- Meaning emerges over time
- Behavior is adversarial
- Individual actions are ambiguous
- Intent is revealed only through sequence and interaction
In other words: it fails precisely where attackers operate.
You cannot reason your way into understanding a world your systems fail to perceive.
World Models Are About Perception, Not Just Planning
Much of the current discussion frames world models as tools for planning — simulating futures, evaluating actions, choosing paths.
But before planning comes perception.
A world model first answers questions like:
- What normally happens here?
- What tends to follow what?
- Which transitions are stable?
- Which sequences feel wrong — even if no rule is violated?
That kind of understanding does not come from retrieval.
It comes from exposure, compression, and internalization.
This is why world models are learned, not programmed.
LogLM as a Domain-Specific World Model
The DeepTempo LogLM was built to learn one specific world:
The world of humans and machines interacting inside modern enterprises — benign and malicious alike — as revealed through their observable footprints.
It does not model “the internet” or “everything.” It cannot tell you anything about Shakespear and it couldn’t have helped me to write this blog.
It models the part of reality where security impacts occur and decisions are made.
LogLM learns:
- How people, services, and systems normally interact
- How those interactions evolve over time
- How benign activity differs from adversarial activity
- How small deviations compound into meaningful threats
It does this without rules, signatures, or exhaustive labeling — by learning directly from long sequences of real behavior.
That is what makes it a world model.
Why This Has to Be Learned, Not Assembled
Consider the alternative.
Imagine trying to give a general-purpose LLM enough context to decide whether an attack has occurred over the last 30 days in a large enterprise.
What would you load into the prompt?
- Millions or billions of events?
- Summaries of summaries?
- A list of known attacks?
- A snapshot of “normal”?
All your venture funding. All your customers’ tokens.
None of these capture:
- Non-local effects across users and systems
- Delayed cause and effect
- Adaptive adversary behavior
- Subtle escalation patterns
To generalize to types of attacks rather than known signatures, the model would need to have already seen enormous numbers of examples, across user environments and time. Scaling laws apply here just as they do everywhere else.
That knowledge cannot be injected through a context window.
It must already exist.
Embedded Context Is World Knowledge
Here is one important distinction:
- Context windows replay facts
- World models encode understanding
The DeepTempo LogLM compresses weeks of activity into internal representations that already reflect:
- What is typical
- What is rare
- What is unstable
- What is concerning given everything that came before
By the time a decision layer is involved, the world is already understood well enough to reason about.
Why a Cyber World Model Is Now Essential
Modern attacks are themselves built with the help of reasoning models; as we’ve shared many times, AI powered attacks are real and increasingly are the default method by which attackers attack. As a result, attacks are already much more likely to be:
- Slow
- Coordinated
- Adaptive
- Designed to evade the detection logic of security systems
Defending against today’s attacks requires more than detection logic.
It requires situational awareness — a lived understanding of how the system normally behaves and how that behavior is changing.
That is what world models like the DeepTempo LogLM provide.
And in cybersecurity, that world must be learned from the only place it reliably reveals itself: the continuous footprints of humans and machines interacting at scale.
Closing Thought
World models don’t need to model everything. They need to model the right world.
In cybersecurity, the world is not the network. It is the behavior of people and systems — unfolding over time. Network logs are the best place to start to catch these behaviors, their sheer volume and relatively constrained grammar were ideal starting points for the self supervised learning that trained our LogLM.
Our LogLM is a world model for cyber. Available now for free look back analysis. Upload some flow logs and see what it sees before any adaptation to your environment. Or just get in touch and we will happily help you to deploy it yourself; see what you’ve been unable to perceive, and better protect yourself from ever more prevalent AI powered attacks.
Get in touch to run a 30 day risk-free assessment in your environment. DeepTempo will analyze your existing data to identify threats that are active. Catch threats that your existing NDRs and SIEMs might be missing!
