HackNotice Premium taps machine learning to tell you exactly what was exposed for $50 per year

HackNotice today launched HackNotice Premium, a threat monitoring service with real-time insights into potential breaches and data leaks. The premium version can tag the specific personal identifier (email, credit card information, social media account, and so on) that was compromised. HackNotice Premium also taps machine learning to gives consumers personalized response recommendations. The subscription service costs $4.95 per month or $49.95 per year (if you sign up this year, you’ll get 50% off the first six months).

HackNotice first launched in July 2018 as a free service for Android, iOS, and the web. Based in Austin, Texas, HackNotice monitors when hacks occur, notifies you about the ones that affect your accounts, and guides you through the process of recovering from, and reducing the risk of, identity leaks. The service continuously aggregates data across the dark web, public sources, and official disclosures, indexing breach disclosures and data leaks.

HackNotice Premium goes further with the following features:

  • Compromised Record Tagging: Alerts will include the specific details that were likely compromised, such as email addresses, passwords, home addresses, phone numbers, or credit card information.
  • Personalized Response Recommendations: When notified, users will receive actionable recommendations for how to recover based on the type of hack and how their records were impacted.
  • Increased Monitoring Capacity: Users can monitor 20 personal identifiers online, up from three with the free Personal Service, on up to 100 websites, specifying a domain of their choice or selecting from the suggested financial, social media, ecommerce, and other sites that typically host sensitive information.

“Historically, consumer services have failed to provide the insight and support needed to effectively and quickly respond to a data leak,” HackNotice CEO Steve Thomas said in a statement. “Hackers are only getting savvier, and as we live our lives increasingly online, it’s critical that consumers have the information they need to protect themselves.”

Personalized Response Recommendations

The free version provides alerts for hacks and leaks, along with general recovery actions, but does not share specifics around what information was exposed or recommended security recovery actions. So we asked how exactly the premium version’s personalized response recommendations work.

“We collect every source of information that we can about a data incident, including official state breach disclosures (sometimes several different disclosures from different state governments) and news articles,” Thomas told VentureBeat. “We use that text with machine learning to identify over 20 different types of information that could have been exposed, such as social security numbers, financial details, names, and email addresses. We map each type of information to specific threats or actions that hackers could take to exploit the information, take over accounts, or commit fraud. We also map each type of information to recovery actions that the user can take to protect themselves from those threats.”

HackNotice Premium

Say, for example, a breach at a company exposes your credit card number. Hacknotice Premium would recommend that you report the number as stolen, monitor your financial information, and check your credit report.

HackNotice analzes the more than 44,000 data breaches in its database with machine learning. As for the 15 billion data leak records, they are currently being analyzed algorithmically but will soon be analyzed with machine learning. Thomas says HackNotice started with an algorithm that uses a classifier based on a training set where data incidents were manually tagged with what data was exposed. The team is now moving to a statement confirmation analysis algorithm.

HackNotice Premium mobile

The data incident collection, threat modeling, and remediations are all built in-house. HackNotice partnered with Stylo to provide the machine learning modeling and improvement.


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