Rating engines are systems designed to calculate premiums or prices for products and services, primarily used in the insurance and financial sectors. They assess various factors like risk, customer profiles, and policy details to determine accurate pricing. These engines are crucial for offering competitive rates, ensuring profitability, and adhering to regulatory standards. In addition, they are flexible, scalable, and integrate seamlessly with other business systems, providing real-time calculations to support decision-making processes.

Rating Engines

3D Print Service Bureaus

4G and 5G Private Mobile Network Services

4PLs

5G Enterprise Data Services

5G Network Infrastructure for Communications Service Providers

A/B Testing Tools

AI Avatars

AI Code Assistants

AI Offerings in CSP Network Operations

AI Writing Assistant Software

AI and Data and Analytics (D&A) Service Providers

AI in CSP Customer and Business Operations

AI-Augmented Software-Testing Tools

AI-Enabled Interview Intelligence

AI-Enabled Translation Services

AIOps Platforms

API Management

API Protection

Absence Management Software

Access Management

Account-Based Marketing Platforms

Account-Planning Tools

Accounting Practice Management Software (APMS)

Accounts Payable (AP) Recovery Audit Services

Accounts Payable Invoice Automation Solutions (Transitioning to Accounts Payable Applications)

Active Metadata Management

Ad Tech Platforms

Adaptive Project Management and Reporting

Advanced Analytics Service Providers for Marketing

Advanced Contract Analytics

Advanced Distribution Management Systems

Affiliate Marketing Platforms

Alternative Legal Service Providers (ALSPs)

Analytics Query Accelerators

Analytics and Business Intelligence Platforms

Analytics and Decision Intelligence Platforms in Supply Chain

Anti Money Laundering (AML) Software

Applicant Tracking Systems (ATS)

Application Crowdtesting Services

Application Delivery Controllers

Application Development Life Cycle Management

Application Integration Platforms

Application Platforms

Application Portfolio Management Tools

Application Security Posture Management (ASPM) Tools

Application Security Testing

Application Testing Services, Worldwide

Artificial Intelligence Applications in IT Service Management

Asset Investment Planning Solutions for Energy and Utilities

Asset Leasing Software

Asset Performance Management Software

Audience Intelligence Platforms

Audit Management Solutions

Augmented Analytics

Augmented Data Quality Solutions

Augmented Reality Development Software

B2B Customer Community Platforms

B2B Gateway Software

B2B Marketing Automation Platforms

B2B Profit Optimization Software

BPM-Platform-Based Case Management Frameworks

Background Check Software

Backup as a Service

Banking Payment Hub Platforms

Bankruptcy Software

Barcode Software

Benchmarking Services for Transportation Rates and Logistics Performance

Benefits Management Software

Blockchain Consulting and Proof-of-Concept Development Services

Blockchain Platforms

Blockchain as a Service

Brand Advocacy Services

Brand Compliance Software

Brand Protection Software

Brand Strategy Agencies

Breach and Attack Simulation (BAS) Tools

Business Continuity Management Program Solutions

Business Process Automation Tools

Business Process Management Platforms

Business-Outcome-Driven Enterprise Architecture Consulting

CPS Protection Platforms

CRM Customer Engagement Center

CRM and Customer Experience Implementation Services

CRM in Life Sciences

CSP 5G Core Network Infrastructure Solutions

CSP 5G RAN Infrastructure Solutions

CSP Customer Management and Experience Solutions

CSP Revenue Management and Monetization Solutions

CSP Service Design and Orchestration Solutions

CSP Service and Network Assurance Solutions

Candidate Relationship Management (CRM) Software

Capital Investment Management Software

Carbon Accounting and Management Software

Carbon Offset Platforms

Cash Flow Management Software

Category Management Solutions

Cell and Gene Therapy (CGT) Platforms

Channel Integration Software

Chaos Engineering Tools

Citizen Service Delivery

No, but both are a bad sign.  Make sure you interview the company before accepting an offer.  And do some background checks with the local IT community.  Find out what the culture is like.  What can you expect the working conditions to be like.  How respected is management by the engineers..  It's like dating..

2023 Q3

2023 Q4

2024 Q1

2024 Q2

No, but both are a bad sign.  Make sure you interview the company before accepting an offer.  And do some background checks with the local IT community.  Find out what the culture is like.  What can you expect the working conditions to be like.  How respected is management by the engineers..  It&#39;s like dating..  

Have you experienced any of these in your first week in a new company or department?

It's a combination of prompt engineering and trustworthy outputs.

Prompt engineering is a skill all employees should be building, but there is an appetite to push it towards a dedicated role, which is a very short sighted as it will lead to bias (both concious and unconcious)

Trustworthy outputs falls at the other end of the spectrum and is simply that we can't take anything that Generative AI delivers at face value. It needs to be valdiated and checked.

Tools to agnostically evaluate, privacy, transparency, bias and model energy consumption. A sort of Nielsen rating for retail but for LLMs.

The biggest gap isn&#39;t so much in the technology, but not being able to trust it. In my personal experience, there are way too many hallucinations that occur to make it trustworthy for anything more than general admin type of functions.

That said, I do think there is a place for it to help in the SOAR front and for finding connections in threats that a human wouldn&#39;t see. In addition, it should also be able to help find ways to remediate and clean up inefficient implementations of products like firewalls, etc. 

What’s the biggest gap you see right now in generative AI tools? What type of tool do you wish was on the market?

The problem with vulnerability management sounds complex but it isn’t. From a data standpoint, there are only 200K known, unique vulnerabilities. The challenge is understanding which one is a known exploit, labeling the exploit as a remote code execution (RCE) and determining which one is trending or trying to do ransomware; that’s where AI models can help. The marketing term for it is “knowledge graph” but what you're really doing is indexing everything and ranking it all, like a search engine. 


That's what we have done at RiskSense: We took what Google did 20 years ago with apps and authorities from a page rank perspective, and then re-implemented that for cyber today from a vulnerability and threat management perspective. For example, one metric is called Term frequency–Inverse document frequency (tf–idf) and measures how often or how rarely the term occurred. 


So using that approach, we went back and looked at all exploit code that's committed to Metasploit and PRCs. Rather than taking the tags at face value, we studied those exploits ourselves and labeled them using Natural Language Processing (NLP). It used to take four days for an analyst to understand an exploit and label it; today we can do it in four seconds. That's a huge win for us. We run the models on a continual basis now and when a new exploit comes, we label it. If we don't get the accuracy then a human looks at it. It's a very typical live example.

The big challenge with vulnerability management—even with the small number we have—is narrowing down which ones are most important. I understand that every vulnerability doesn't have the same level of importance within the environment and we have the rating system, etc. But when you have thousands of machines sitting in the environment, imagine having the ability to say, "This is important, but the reality of the situation is that you need to patch these 47 machines out of your fleet of 15K right now. Because as I look at the environment, these are the ones that either have the most critical data or are on an exploitation path so this needs to be closed." Then I could say to my team, "This is where I really need you to focus." Think about what that does in terms of true risk management in the environment and the level of effort required. I am doing that manually now and there are just not enough hours in the day.

The problem with vulnerability management sounds complex but it isn’t. From a data standpoint, there are only 200K known, unique vulnerabilities. The challenge is understanding which one is a known exploit, labeling the exploit as a remote code execution (RCE) and determining which one is trending or trying to do ransomware; that’s where AI models can help. The marketing term for it is “knowledge graph” but what you&#39;re really doing is indexing everything and ranking it all, like a search engine. 


That&#39;s what we have done at RiskSense: We took what Google did 20 years ago with apps and authorities from a page rank perspective, and then re-implemented that for cyber today from a vulnerability and threat management perspective. For example, one metric is called Term frequency–Inverse document frequency (tf–idf) and measures how often or how rarely the term occurred. 


So using that approach, we went back and looked at all exploit code that&#39;s committed to Metasploit and PRCs. Rather than taking the tags at face value, we studied those exploits ourselves and labeled them using Natural Language Processing (NLP). It used to take four days for an analyst to understand an exploit and label it; today we can do it in four seconds. That&#39;s a huge win for us. We run the models on a continual basis now and when a new exploit comes, we label it. If we don&#39;t get the accuracy then a human looks at it. It&#39;s a very typical live example.

Is AI the solution to vulnerability management?

Best Rating Engines Reviews 2024 | Gartner Peer Insights

We started an initial discussion meeting in order to understand business needs and how to replace our old system that was out of maintenance for a long time. 
To possible strategies have been evaluated: upgrade old our existing tool or to choose new tool like TigerLab. My roles was to helped my customer to evaluate  the functionalities an challenges of TigerLab and my role was also to support purchase manager in negoziathion phase.

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TigerLab

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