Intelligent defense against AI-powered attacks
Detects and stops AI cyberattacks on your business infrastructure.
AI agents can explore in parallel, share discoveries, divide work, adapt, and combine capabilities before your company's traditional security systems even realize that these individual events are connected.
One event tells you what happened. The connections reveal what might be happening.
AI-powered attackers can use autonomous agents to explore infrastructure, exchange discoveries, and adapt their behavior. Events that look unrelated may belong to a larger operation.
- Different identities.
- Different resources.
- Different timestamps.
- Different actions.
- 12:04:01.002actor_041repo/payments-apiread
- 12:04:01.438actor_178artifact-07write
- 12:04:02.019ci-runner-12build-cachereadroutine
- 12:04:02.611actor_223iam/role-deploydenied
- 12:04:03.744actor_152artifact-07read
- 12:04:04.102backup-job-3snapshotscreateroutine
- 12:04:04.529actor_044storage/exportslist
- 12:04:05.033actor_197storage/exportsread
- 12:04:05.618actor_082artifact-07write
Illustrative · synthetic events. Relationships are inferred from shared resources and sequence, not observed directly.
Don't just take our word for it.
Run a synthetic experiment. Watch autonomous agents interact. Explore the evidence behind Inwom's conclusions.
Run the Experiment (opens in a new tab)Synthetic environment · No external targets · No signupObserved events
Trusted infrastructure records what happened: who touched which resource, and when. Nothing is inferred yet.
- observed22 events across 9 identities
- observedartifact-07 written once, read by others
A real demo on AWS, run in an authorized sandbox with no real data: five agents each touch one resource and pass a clue to the next. Individually routine. Inwom reconstructs the connected chain, and separates what was observed from what was not established.
Intelligence without evidence is just another guess.
Inwom keeps four things apart, so any conclusion can be traced back to what was actually seen.
- 01 · Observed
Facts
What trusted infrastructure recorded: an identity, a resource, an action, a timestamp. No interpretation.
- 02 · Inferred
Relationships
Links reasoned from the facts: a shared artifact, a hand-off, a sequence. Always marked as inferred.
- 03 · Hypothesis
Coordination
A possible explanation for the relationships. Testable, and open to being wrong.
- 04 · Supported by evidence
Malicious behavior
Reached only when the evidence supports it. Coordination alone does not qualify.
Coordination ≠ maliciousness.
CI workers coordinate. Deployment systems share artifacts. Distributed infrastructure moves together, for good reasons.
Correlation ≠ intent.
A pattern can raise a question. Only evidence can answer it.
- Agent transcripts are claims.
- Trusted runtime activity is evidence.
- Inferred relationships remain inferred.
- Communication is not authorization.
- AI reasoning never becomes authority.
First, understand the attack. Then, change the outcome.
- TodayMVP · available for AWS
Detection, Intelligence & Defense
Reconstruct coordinated, AI-driven activity from observable evidence, understand how it unfolds, and defend cloud infrastructure on AWS.
- Observed events and identities
- Reconstructed relationships and evidence
- Infrastructure defense for AWS
- NextExpanding
More clouds. More infrastructure.
Bring the same detection and defense to other clouds and infrastructure, and develop intelligent defensive agents designed to help contain attacks.
- Other cloud providers
- Other infrastructure
- Defensive agents, under development
- HorizonA direction, not a product
Autonomous Defense
Defense that understands how an attack unfolds well enough to help change its outcome.
- An ambition, stated plainly
Status labels are literal. Today is the MVP, available on AWS. Next is where it expands. Horizon describes intent, not capability.
Attackers are evolving. Defense must evolve too.
Security was built to identify suspicious activity. We're building toward systems that can understand how AI-powered attacks unfold — and help defenders respond.
Outthink AI attacks.
Straight answers.
What is Inwom?
Inwom builds security intelligence designed to understand attacks powered by artificial intelligence. Its initial specialization is detecting and reconstructing potentially coordinated activity across autonomous AI agents, infrastructure identities, and resources. Longer term, Inwom is working toward intelligent defensive agents that help contain attacks.
What does Inwom do today?
Today Inwom is running for AWS: it connects observed events across identities and resources, reconstructs relationships between them, shows the evidence behind each conclusion, and defends cloud infrastructure. Support for other clouds and infrastructure is next, followed by intelligent defensive agents designed to help contain attacks.
Is Inwom only about AI agent swarms?
No. AI-powered attacks are the larger category Inwom is built for. Coordinated activity across autonomous AI agents is its initial technical specialization, not the boundary of the company.
What problem does Inwom address?
AI-powered attackers can use autonomous agents to explore infrastructure in parallel, exchange discoveries, and adapt their behavior. Each event can look unremarkable on its own, and events that seem unrelated may be part of a larger operation. Inwom is built to connect that evidence and show where coordination may exist.
How is Inwom different from prompt-injection firewalls and LLM guardrails?
Guardrails and AI firewalls inspect individual prompts, responses, or tool calls. Inwom works one level up: it reconstructs relationships across many agents and identities over time to look for propagation, collective structure, hidden communication channels, and coordinated pressure on security boundaries. It is designed to complement guardrails, identity, and policy controls, not replace them.
Does Inwom replace my SIEM, EDR, or XDR?
No. SIEM, EDR, and XDR provide event and endpoint visibility. Inwom uses trusted infrastructure evidence to connect activity across multiple identities and AI agents and to explain relationships those systems may represent as separate events.
Does coordination mean an attack?
No. Coordination is not maliciousness, and correlation is not proof of intent. CI workers, deployment systems, and distributed infrastructure move together for legitimate reasons. Inwom keeps observed facts, inferred relationships, hypotheses about coordination, and evidence-supported malicious behavior as separate layers, and exposes the evidence behind each.
Why does Inwom treat agent transcripts as claims?
An agent's own narrative of what it did is not proof that it happened. Inwom's principle is evidence before inference: agent transcripts are claims, trusted runtime and infrastructure activity is evidence, inferred relationships remain explicitly inferred, and AI reasoning never becomes authority.
Which OWASP Agentic Top 10 risks does Inwom address?
Inwom focuses on the risks that only appear across multiple agents: Insecure Inter-Agent Communication (ASI07), Cascading Failures (ASI08), and Rogue Agents (ASI10), along with relationship-level evidence for Tool Misuse (ASI02), Identity & Privilege Abuse (ASI03), and Memory & Context Poisoning (ASI06).
Can I try Inwom?
Yes. The Inwom Playground at playground.inwom.com is a free, public, synthetic environment where you can run an experiment, watch autonomous agents interact, and explore the evidence behind Inwom's conclusions. No signup is required and there are no external targets. Teams that want to see Inwom on real AWS infrastructure can request a demo on inwom.com.
Who is Inwom for?
Security teams, platform teams, and AI teams that deploy or are exposed to autonomous AI agents — coding agents, browser agents, support agents, or multi-agent frameworks — and want to understand how AI-powered attacks could unfold across their identities and infrastructure.