Exposure Management
Continuous answer to one question: what could an attacker reach today, and which of it matters. Not a scanner — the layer that turns every scanner's output into a ranked, owned queue and keeps it current as the estate changes.
- Unified findings from every connected tool, deduplicated on rule identity and asset
- Attack surface mapped from network, gateway and cloud topology — what is reachable, from where, with or without credentials
- Composite risk scoring from exposure, exploitation activity, criticality and verified control state
- Attack paths across assets, so a low-severity foothold leading somewhere valuable is ranked accordingly
- A CTEM loop with a maturity measure, so the programme itself is assessed and not just the estate
What it does not do
- Replace your scanners. It reads them; they keep doing their job
- Discover what no connected tool can see — coverage gaps are surfaced, not invented
- Rank by severity, which is the practice it exists to end
Continuous Threat Modeling
Most threat models are written once, reviewed at design time, and never opened again — so the attack paths a team reasoned about carefully have no connection to the findings that later arrive against the same system. Correlation closes that loop: a finding can be recognised as a path somebody already predicted.
- STRIDE-based models held as structured data rather than documents, so they can be queried
- Findings matched to the attack paths a model already anticipated — predicted, not surprising
- Architect review workflow, with models versioned against the design they describe
- Gaps surfaced where a service has no model, or a model has no corresponding controls
- Runtime correlation: whether the mitigations a model assumed are actually in place today
What it does not do
- Generate a threat model nobody reviewed. A model is an argument, and it needs an author
- Replace an architecture review — it makes its conclusions durable and queryable
GRC & Audit Evidence
The difference between a GRC tool and this one: evidence is derived from live findings rather than typed into a register. A register describes what you intended; derived evidence describes what is true this morning.
- One control mapped to every framework that references it, so a single fix moves several at once
- Coverage as a continuous number — drift shows in week two rather than month eleven
- Evidence traceable to the findings and assets behind it, which is what survives an auditor's follow-up
- Risk acceptances with an owner, an expiry and a review that actually occurs
- Audit log of who changed what, append-only and enforced by the database
What it does not do
- Certify you. It produces evidence; an assessor still assesses
- Cover controls with no technical signal — policy and training evidence stays where it lives
- Replace your GRC platform if you have one. It can feed it instead
AI Infrastructure Security
Teams are shipping agents, models and vector stores faster than any inventory process keeps up with, and the tools listed on the Platform page are largely blind to them. AI-Interceptor finds that estate and assesses it — then feeds it into the same risk model as everything else, because an exposed agent is not a separate category of problem.
- Discovery of agents, model endpoints, LLM gateways and vector databases, including the ones nobody registered
- Agent permission analysis — what each agent can reach, and what its blast radius is if it misbehaves
- Behavioural probing with evidence rather than inference, including prompt injection and data leakage
- Model supply chain: unsafe serialisation formats, untrusted sources, integrity
- Drift detection between scans, so a posture that quietly worsens is noticed
- Mapped to MITRE ATLAS and the OWASP LLM Top 10
What it does not do
- Sit in the request path. It is a scanner, not a runtime guardrail or proxy
- Judge model quality, bias or accuracy — those are real problems and they are not this one
- Assess an AI system it cannot reach; discovery is bounded by what the control plane exposes
Cloud Security Posture
If you run a CNAPP, keep it — it correlates deeply inside cloud and does that better than a general platform will. What it cannot do is join a cloud misconfiguration to the application that depends on it, the code finding in the same service, and the business unit that owns both. That join is what is added here.
- Misconfiguration and posture findings from AWS, Azure and GCP in one queue with everything else
- Identity and entitlement exposure — which workloads assume which roles, and what those roles can reach
- Containers and Kubernetes posture alongside the images and repositories they came from
- Cloud assets resolved to applications and business services, so ownership is not a manual lookup
- Internet exposure computed from actual topology, which is the largest multiplier in the risk score
What it does not do
- Replace a CNAPP for in-cloud depth. It reads from yours and adds the cross-boundary joins
- Enforce or remediate in your cloud account. It reads; changes stay with your teams
- Cover accounts nobody connected — unmapped subscriptions are reported as a gap
Why they belong together
Five capabilities that would be five silos anywhere else
Bought separately, each of these arrives with its own asset list, its own idea of severity and its own dashboard — and reconciling them becomes a permanent job. On one model, each makes the others more accurate. Some examples that only work because the data is shared:
Start with one. Add the rest when you need them.
Most customers begin with exposure management, because it is where the noise is. Nothing needs re-integrating when the next capability is switched on — same model, same ownership map.