Artificial Intelligence & the Security Market

A glimpse into how two new products for intrusion detection and entity resolution are using AI to help humans do their jobs.

Machine learning, advanced heuristics, or artificial intelligence: the language is shifting but the idea that computers are taking a greater role in automating security functions is moving straight ahead. Two new product announcements demonstrate that direction in very different ways.
Aella Data and Senzing each brings a product based on AI technology to market, and in some ways the products could not be more different in purpose, intended audience, or business model. But both share a critical similarity: Each uses AI to correlate data from many different sources to present information that assists humans in doing their jobs.
Aella Data came out of stealth mode just before this years RSA Conference. The companys product, Starlight, is billed as a virtual security analyst able to perform a breach detection across massive networks. This week, the company added multi-tenancy to the product in Starlight 2.0, which is aimed at managed security service providers that want to add the capability to the set offered to their customers, or very large enterprises that need a single intrusion alert system that can scale to global size.
In a demonstration for Dark Reading, Aella Data showed the capability to monitor and analyze data on a customer-by-customer basis while also aggregating total provider network data for the MSSP staff. A graphical interface showed alerts and warnings while also pointing staff at details such as known and suspected malware C&C servers accessed from within the network, network activity by individual endpoint devices, and both known and suspected malware active on the network.
While Starlight is intended to augment the expertise and activity of human security staff, Senzing Software hunts for bad guys in ways that are essentially impossible for humans to duplicate. The companys software uses AI for non-obvious relationship awareness in the service of entity resolution — essentially, figuring out whether a group of entries that might share some characteristics are actually all one entity.
Senzing, spun out of IBM two years ago and coming out of stealth this week, is led by founder and CEO Jeff Jonas, a former IBM Fellow and chief scientist of context computing. Jonas spoke to Dark Reading in a telephone interview, and says that the technology the companys products are built on can be used to resolve multiple intrusion attempts to an actual individual or organization, clean up a heavily-duplicated contact list, or de-duplicate entries in a states voter registration roll.
The AI technology required for entity resolution uses entity-centric learning, Jonas says, explaining that, in the Senzing software, entities are only snapped together if the system is sure — [and] uncertainty is scored as a possible match that can then be reviewed by human operators.
Asked about the difference between machine learning and AI, Jonas says that machine learning involves systems that use new data to make themselves more accurate, while AI describes systems that are human smart, though he says that intelligence may lie in a very narrow context.
Both Aella Data Starlight and Senzing software are available now. Senzing offers a free version of its software for individuals with up to 10,000 records to match.
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