Close the loop on risk.
Before and during the attack.
A fleet of AI agents on the stack you already own. Test from the outside like an attacker, reduce exposure before the attack, investigate and respond during it, and feed every outcome back into your defenses.
Twin
The agentic defense loop: before and during the attack
Reduce exposure
Find undefended CVEs, hunt zero days, and map breach paths against the defenses you already own.
Agentic defense loop
Agents reason over the digital twin the Security Data Fabric keeps current, then prioritize, investigate, respond, and verify, learning from every cycle.
Investigate & respond
Eliminate false positives, correlate campaigns, and drive incident response with agents that never queue.
One loop. Three measurable outcomes.
Your controls will keep multiplying. Rules can't orchestrate them.
Nobody is shrinking the stack. The question is who runs it.
Assets, identities, exposures, controls, and detections in one live model, kept current by the Security Data Fabric as your environment changes. Logs stay where they live.
Agents build the investigation on first encounter, and each one has allowed tools, evidence requirements, and an approval gate.
Kairo tests whether a path is reachable, whether your controls break it, and whether the fix closed it. Same CVE, two systems, two answers.
What is breachable before an incident and what an incident can touch come from the same model, across your whole stack.
One security model over the stack you already own.
Exposure asks what is breachable. Investigation asks what this incident can touch. Both answers come from the same live model, across cloud, identity, endpoint, network, on-prem, and the SIEM.
The digital twin continuously models how identities, assets, controls, and exposures interact, so agents can reason about reachability before an attacker does.
Five agents. One continuous loop.
With the mesh in place, here is what runs on top of it. Vector tests from the outside; the rest reason over what it finds. Adopt any agent on its own, or run them together as one continuous loop. Each reasons over the same context, so the more you connect, the smarter every agent gets.
Autonomous Red Team & Exposure Validation
Probes your approved external scope the way an attacker would, using new TTPs, fresh CVEs, and AI-driven techniques. Every finding is checked against the controls you have deployed, the risks your tools already report, and your application and infrastructure topology, so you act only on what an attacker could use.
Breach Path Detection & Disruption
Models how exposures, identities, workloads, and controls chain into real cross-domain attack paths. Tests whether your deployed controls actually break the path, stages the compensating fix through tools you already own, then re-tests the path to prove it is closed. Same CVE, two systems, two different answers.
Exposure & Vulnerability Prioritization
Validates exploitable risk across VM, cloud, identity, AppSec, and business context. Reduces millions of findings to the small subset that is exploitable, reachable, and undefended, so teams focus on breachable issues, not raw severity.
Zero-Day & Emerging Threat Response
When a new CVE drops, Quell answers whether it creates a reachable path in your environment, then recommends the compensating control that closes it first. Exposure validation at the speed threats now move.
Alert Triage & Investigation
A two-stage investigation. Stage one returns a true or false positive verdict with confidence. Confirmed positives escalate to stage two, which builds a MITRE-mapped attack timeline, blast-radius analysis, and tiered response recommendations.
What's automated, and what stays human.
Works on day one. Open any row to see the mechanics.
Layer 0Query where the data lives GAFederated search across 150+ tools, no centralization.
- Tier-1 signals stay where they are generated (an EDR alert, an NDR anomaly, an identity risk event, a cloud audit signal). Your existing tools keep doing the detecting.
- Tier-2 federated queries sweep recent Tier-1 findings across sources, joined by entity and time window, and escalate when signals converge.
- One question is answered by querying every relevant source at once, so coverage holds when a source is quiet or incomplete.
Layer 1Every alert to a verdict GATrue or false positive, with confidence, automatically.
- Prepare: extract the triggering event, enrich entities, and check the Security Data Fabric for entity baselines, detection-rule priors, and cached IoC verdicts.
- Assess: answer a merged set of your dynamic playbook questions plus OCSF-framework questions, accumulating weighted true and false positive signals.
- Verdict: compute scores and render a true or false positive with high, medium, or low confidence, then either close it or escalate to Layer 2.
Layer 2Full attack timeline and blast radius GAMITRE-mapped investigation reasoning, end to end.
- PEAK (Prepare, Execute, Act) decomposes hypotheses, runs the evidence-gathering queries, then renders verdict and recommendations.
- ABLE (Actor, Behavior, Location, Evidence) maps each hypothesis to MITRE ATT&CK techniques and the exact data sources where evidence should appear.
- Blast radius: other users from the same source IP, other targets by the same actor, other hosts with the same file hash, correlated across 1h / 24h / 7d windows.
- Output: a MITRE-mapped timeline and tiered recommendations (0-1h, 1-24h, 1-7d, 7-30d), plus the detection gaps it found.
Analysts get decisions, not another queue.
Most AI copilots hand you a summary. Tuskira hands you the decision and the receipts. Every investigation ends in a structured, schema-validated report your case management can consume directly, carrying the evidence ledger, cumulative scores, a confidence band, and the full reasoning trace. A senior analyst can resume any case in the Analyst Copilot, pivot the agent, query live tools, and challenge the call.
The question every CISO is asking about agentic security. Tuskira's answer is built into the architecture, not left to a setting.
- Forms competing hypotheses and disproves the alternatives before it commits
- Chooses which evidence to pull and records every query
- Builds the investigation and playbook on first encounter, then writes the explanation a human reads
- What is reachable. The graph and the Kairo path model do.
- What it may execute. Policy does, through your own controls.
- Where your logs live. They stay in place.
Built on memory, not prompts.
The outcome, the playbook, and the evidence are written back to the Security Data Fabric, so the next cycle starts from a better model than the last one.
A proprietary hypothesis-decomposition method (PEAK-ABLE) that maps every investigation step deterministically to MITRE ATT&CK, not a generic chain-of-thought.
Every investigation feeds entities, outcomes, and prior probabilities back into your own twin. Future investigations check it first, improving consistency and cutting cost.
Tuskira evaluates your existing controls, identifies which risks are defensible, and recommends precise changes to collapse validated breach paths through tools you already own.
Resume any agent session with the full transcript and live tool access. Tuskira treats the analyst as a peer of the agent, not a rubber stamp.
Transparent by design, auditable by default.
Your telemetry stays in your boundary, always. Tuskira queries scanners, controls, logs, and SIEM in place and never copies them into a new lake.
Every prompt, tool call, evidence artifact, and verdict is logged per tenant and reviewable without Tuskira in the room. Containment always waits for a human.
The exact prompt, every tool call, every evidence artifact, and every model response, captured per case for analysts and machines alike.
Isolated credentials, case store, and audit log per tenant. Built for MSSPs and regulated industries from the ground up.
Customers can audit their own tenant traffic without Tuskira operator involvement. Every action is attributable and timestamped.
See your stack reason at machine speed.
Bring your own tools. Keep your data in place. Watch Tuskira turn scattered signals into verdicts.
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