We Contextualize Data

 

Light-Touch Data Integration

You already have the data you need to quantify risk. The challenge is that it is typically isolated in multiple compliance tools, management systems, or other response silos—and not clearly contextualized to specific risk exposures and impacts from a business-oriented perspective.

Our on-premises or cloud solution uses your existing enterprise data to enable an enterprise view of Digital Risk Impact—constantly measuring your network for exposures and identifying the business units creating the risk. And because we use only light-touch metadata queries, no sensitive data like user activity, file content, or net flows ever touches our platform.

 

 

 

 

 

 

 

 

 

 

 

Our family of data connectors spans your enterprise, including your firewalls, gateways, directory servers, ticketing systems, etc. Custom connectors are also a snap to build, thanks to RESTful API and JavaScript formats.

We include many out-of-the-box external data sources, to enhance your internal view.

Each additional data source provides an additional dimension to your enterprise’s risk exposure, but you can get started with as few as three.


We Stay Flexible

Extensible Ontology

Risk data uses an extensible object-oriented ontology to determine how exposed the enterprise is to cyber-attacks or other digital incidents. Think of them like building blocks to communicate various risk incidents.

First Line cybersecurity practitioners and Second Line business leaders can both “see” which scenarios are most likely to be the cause of a breach or other digital risk incident.

Emergent is the only scenario-focused product that lets you start by talking to your business about the scenario, and then drill into the data to find out what needs to be addressed first.

 

Open Risk Frameworks

Many existing products are rigid in their use of risk analysis frameworks and require integration projects that shift an organization’s processes to adopt their preferred framework. The Emergent product allows flexibility of data input and the ability to view the output through many lenses. This enables greater visibility and framework clarity.

Risk metrics are tagged by an ever-growing library of frameworks, including:

  • FAIR Institute
  • NIST Cybersecurity Framework
  • FFIEC Cybersecurity Assessment Tool
  • Attack Surface
  • Killchain

We use AI to achieve scale

 

We SWARM so you don’t have to

To manually get a handle on the universe of digital risk, you would need to hire a swarm of risk analysts watching every technology and data feed and thinking of every possible “bad day” scenario at once. We use swarming artificial intelligence and machine imagination to do it for you.

Swarming is a growing field of Artificial Intelligence (AI) for applications that require complex and decentralized reaction strategies. Interesting properties emerge through self-organization (i.e., not explicitly represented or reasoned over at the individual level), as demonstrated in nature and in complex systems, like social insect colonies or traffic patterns.

Many products for risk quantification require large amounts of data and clearly-defined exposures that are assessed individually. Computing risk using a flexible ontology and using Swarming Artificial Intelligence enables risk discovery through emergent features of the system.

 

We worry about your next “bad day” scenario

The Instinct Engine uses Machine Imagination to learn from past events and current business concerns to generate new future risk exposure scenarios. Our platform recombines the risk ontology into new patterns, literally imagining new things that could happen in your environment to expose you to risk.

Never-before-considered scenarios are assessed to elevate those that expose future incidents. This allows companies to see around the corner at emerging risks.

 

 

 

 


The result is real-time risk discovery with dollar-loss impact projection communicated in plain language usable by your business owners.