The Human Managed platform operates on the edges of technology-forward enterprises in the essential services sectors. Some questions at the forefront of every decision-makers’ mind are:

How do I get more value from data using AI?

How do I empower and scale my team’s decision-making with AI?

How do I improve business resiliency with AI?

These are complex questions to answer. But a great silver lining is that you can solve all these problems and more with distributed and scalable data operations. We pick the three ops decisions with the highest impact and value for businesses of all sizes and industries as we enter the age of AI headfirst.

In today’s digital world, data directly impacts your organisation’s top and bottom lines. The last few years have seen explosive growth of AI tools and services accessible to players of all sizes and sectors. Within this fast-changing environment, businesses should focus on the value they can generate from data. The faster, more agile and scalable your dataops is, the bigger your AI-powered opportunities are. For enterprises, this means operationalising any data from anywhere.

Challenges: Now you have the data, what do you do with it?

Businesses are usually adept at identifying the business problems they want to solve, what data is essential for the problem, and where it will come from. The real challenge comes after.

For example, say your cyber goal is to control exposures impacting your most critical service, such as a banking app server. Some key sources that generate data related to assets, violations, and vulnerabilities are managed endpoint detection tools, vulnerability management SaaS, and external threat databases. These data sources could be sensors deployed on-premise, in the public cloud, or in your software provider’s cloud. The data from these sources vary in volume, format, schema, integration methods, etc.

Getting all the relevant data from multiple sources, synthesising them, and processing them through a unified pipeline is infamously challenging to build and scale across numerous use cases. This limits the depth of analysis businesses can run on their data and drives siloed or tool-driven approaches to problem-solving.

Opportunities: Distributed data engineering as input for AI models

The Human Managed DataOps platform ensures that whatever data you want to analyse is continuously collected, processed, and stored to be AI-ready.

One of our customers, a leading ASEAN conglomerate, approached us with a widely shared problem in cyber operations: effective prioritisation. They had struggled with siloed asset databases for 20+ years and managing disparate cybersecurity tools across the public cloud, software vendor cloud, and on-premise. This resulted in manual and slow cyber operations, where many issues slipped through.

The goal was to automatically contextualise and prioritise our customer’s cybersecurity issues as and when the alerts are generated. The customer’s job was completed when they chose 10 data sources to provide us with the required input (alerts, logs, metrics from SaaS and on-prem systems) and context (asset databases, strategies, and business logic).

The HM platform onboarded the customer’s data for continuous cyber operations in less than a month. We catalogued their assets, controls and attributes and structured their cybersecurity alerts, logs and metrics under one data schema and model.

Our solution meant that the variable components — the ingredients — for any analysis by any kind of computing, whether rule-based programming or AI/ML models, were ready.


MLOps: Tuning AI models personalised for your business

Although AI, machine learning, and neural networks are not new, what drove the now-familiar explosion of new AI-powered capabilities is the underlying pre-trained models that picked up speed coined in 2021 as foundation models. Foundation models (sometimes called general-purpose AI or GPAI) are AI neural networks trained on massive raw, unstructured data, often with unsupervised learning, that can be adapted to perform a wide range of general tasks without human intervention.

Foundation model, including generative AI-powered apps beyond our wildest imaginations like ChatGPT, can understand language, generate text and images, and converse in natural language. As AI’s capabilities continue to capture consumers’ hearts and minds, businesses are in a race to decide how best they can adapt AI models in their operations.

Challenges: Now you have the AI models, how do you make it work for your business context?

Foundation models are called ‘foundation’ because they act as the base to build apps that solve different problems. Open and commercial AI models are trained on generalised and unlabelled data, such as data scraped from the Internet or untraceable databases.

Generic AI models, no matter how advanced, will not magically produce accurate and precise outputs suited for your unique business context. For machine learning to be operational, AI models must be trained, tuned, and improved with data, logic, and patterns unique to your business.

Opportunities: Decision models combining tribal knowledge and trending knowledge

The Human Managed MLOps platform tunes foundation AI models with a knowledge base that is unique to each customer’s business context and incorporates a human-in-the-loop feedback cycle to continuously improve and measure the performance of the customer’s personalised AI model. This way, our customers get the best of both worlds: trending knowledge that builds foundation models and tribal knowledge that builds their own context models.

One of the business-critical use cases we apply MLOps for is fraud detection and management for a global banking customer, where we continuously tune AI models with customers’ personalised data to build their own contextualised fraud model that classifies, predicts, or indicates suspicious fraudulent activities.

Over decades of operations, our customer has amassed tribal knowledge and experiences on their fraud landscape and wanted an automated and scalable solution to increase their detection accuracy and decrease their response time.

Examples of their tribal knowledge include indicators of different types of fraud (e.g. repeated withdrawals from the same account on the same day), when a detection alert should be triggered (e.g. withdrawal amount is higher than US$50,000), and what needs to be done when there is a detection (e.g. send a critical alert to fraud investigation unit to investigate suspicious transaction).

All these data points form the unique context of our customer, such as their business logics, prioritised assets, and historical patterns. The Human Managed MLOps platform transforms these data points into structured data and code to form features and labels that tune the customer’s contextualised fraud model.

Once the input data, AI/ML processes, and desired output are aligned, it’s a virtuous circle of human and machine collaboration because ML models improve with more training and feedback. Our customer continues to add more datasets, rules and conditions while the platform continues to learn from data to improve the accuracy of their ransomware model.

IntelOps: Applying AI for better and faster decisions and actions

In today’s turbulent digital world, where speed and agility have become a necessity rather than an aspiration, leaders should pay attention to how they can make their businesses resilient. Business resiliency is about sustaining during unknown conditions and improving and coming out stronger from VUCA (volatile, uncertain, complex, ambiguous) conditions.

Resiliency, when executed right, can turn challenges into opportunities to protect their assets better, create more revenue, and make customers happier. Achieving business resiliency through data is highly impactful for many reasons, not least to understand the changing environment in which you operate, as well as generate intel to make better and faster decisions and actions.

Challenges: Now you have the intel, how do you apply it with the right priorities?

By 2025, it has been predicted that data will be embedded in every decision, interaction, and process (Source: McKinsey, The data-driven enterprise of 2025).

We’ve seen how context can be built through data and models. DataOps prepares the data to generate valuable intel, and MLOps improves AI models to learn from contextualised data.

Getting the proper intel consistently is a challenging feat. However, the benefits of the right intel are limited if you do not apply it to the right problem at the right time. How do businesses ensure this? Even with the best intel made available through DataOps and MLOps, if it is not served to the right audience at the right time, the value of that intel is not realised, and the window of opportunity closes. The challenge here is to bring DataOps and MLOps processes together in a consistent and scalable operational cycle across the business, which we call IntelOps.

Opportunities: Data-driven resiliency with I.DE.A.

Acting with speed and accurate prioritisation is critical to business resiliency. The Human Managed platform services are designed to empower the end-to-end decision-making process, from generating personalised intel to ranked decisions and prescriptive actions. We call this the IDEA Model (Intelligence Decision Action).

Some of our most subscribed services are data-driven asset management, attack surface management, fraud, and security posture management. For every service, our DataOps platform ensures that all of our customers’ data gets processed and analysed to create contextualised insights, which then act as input for the MLOps platform to continuously tune the AI model for more accurate and precise intel. Finally, our IntelOps platform takes contextualised intel to generate ranked decisions and prescriptive actions based on calculated priorities and impact.

One of our customers subscribed to our network security posture management service to move the needle on fixing 40,000 violations that have been open and unresolved for over two years. Contextualised intel on the violations — no matter how organised and easy to understand — did not get their operations team to decide on the next steps because the number of issues was so high.

To make progress on the cyber posture issue, the customer asked for decisions and actions that would have the “biggest bang for the buck”, which is precisely what we delivered. Our IntelOps platform ran computation on 40,000 reported violations on 1000 network segments, protecting around 50,000 assets accessed by 40,000 employees. As an output, we generated four ranked decisions and 16 prescriptive actions that would remove 40 per cent of all violations and improve 100 per cent of customer’s critical assets. The prescriptive actions were delivered through a nudge-based dispatch system to the users who could affect the change.

Get the data right, apply to unique business context, build business resiliency

In conclusion, a business can harness the potential of AI when it has a complete understanding and control of its enterprise data across all sources. Data must be continuously collected, processed and stored to be AI-ready.

For machine learning to be operational, AI models must be trained, tuned, and improved with data, logic, and patterns unique to your business. Finally, applying AI for better and faster decisions means ensuring the company stays resilient against changing VUCA (volatile, uncertain, complex, ambiguous) conditions.

A business can harness the potential of AI when it has a complete understanding and control of its enterprise data across all sources

3 data decisions to make in 2024 for businesses to become AI-native

I wrote about how we integrate Okta and Auth0

 using Auth0’s Social Connector (it runs OAuth2 behind the scenes). Today I will get into a bit more detail about how it’s done behind the scenes.

Our applications are registered directly with Auth0 as a “Regular Web Application”. This first layer of registration allows us to control 

. In other words, only applications authorized by our platform can communicate with our backend.

The second level authentication and authorization that we do is at the 

. This is where the Okta + Auth0 integration comes into play (there’s also 

 but we’ll get into that as a separate topic). The user is redirected to Okta to complete sign-in (enter your username, MFA, etc.) and then returned to Auth0 to continue with the login flow.

 script for that Social connector we configured to talk to Okta. In this script, the Auth0 runtime populates a number of parameters with useful return information from Okta. You get 3 things:

a ctx or context object which gives you access to the id token

and a callback, cb, for you to return a profile object to Auth0 that we use to construct the JWT

The access token is specific to the user who just completed the logon in Okta. We use this access token as part of the Authorization Bearer scheme to call the Okta OIDC UserInfo API (

https://developer.okta.com/docs/reference/api/oidc/#userinfo

). This allows us to fetch additional attributes about the user.

It is highly recommended to use an Okta custom authorization server for this purpose (

https://developer.okta.com/docs/guides/customize-authz-server/main/

). Why? Because the custom authorization server allows more control over the JWT that Okta mints (

). It’s also possible to move some of the custom claims being generated in Auth0’s 

 script to an Okta custom authorization server (it’s really up to you, but we prefer Auth0’s approach because Okta’s expression language pales in comparison to raw Javascript :) )

The return JSON from the Okta API call makes it very easy to parse properties and do additional processing. For example, here is part of our 

 object is what we return to Auth0 as part of our 

 script. This object is used by Auth0 to create the user profile and issue a JWT. This can be done like so at the end of the script:

To add a new claim to the JWT that Auth0 mints, all you have to do is to modify the 

 object to your heart’s content. For example:

 then? That is simply the user’s OIDC token and we can get that from one of the function parameter that Auth0 passes to us i.e. the 

Now that you have both the access token and id token, you can manipulate the final JWT that gets issued! Oh, one more thing — how do you remove a claim? Easy as pie:

When you view your the JIT created user in Auth0, you will see something similar to the image posted below where each field is generated from the 

 object that you manipulate. These fields can be used for 2 purposes — for auditing (you have a snapshot of the user the last time they logged on, so it’ really up to you what fields you want to populate for auditing purposes), and finally for JWT generation.

To get these fields added to the JWT token that Auth0 mints, you use another wonderful Auth0 feature called the login action flow (

https://auth0.com/docs/customize/actions/flows-and-triggers/login-flow

Here’s a sample of how you can do that:

If you need more information, feel free to reach out to us at 

Minting JSON Web Tokens (JWTs) for user level authentication and authorization 

Minting JSON Web Tokens (JWTs)

I’ve joined Human Managed — a data company that empowers decision — in late December 2021 as a front end developer. In a short amount of time, I went through a steep learning curve, working with technologies and processes that I have never worked with before.

One of those is Git, a distributed, open-source version control (VCS) that enables you to:

revert to earlier code version when needed

Back in my college days, I used to code without any version control tool like Git. What I did was copy and paste source code folders and rename it with a version number. My reasoning back then is, since I do code alone, then there’s no point in using a version control system. Lo and behold, this idea backfired to me in my thesis project. There was a time when I needed to update a block of code for a fix, and it took me hours to do so.

I used to agree with the guy at the bottom😂

I knew that I needed to learn git when I joined HM. I won’t be coding alone anymore since I’ll be collaborating with my colleagues. When I was trying to learn it during my first few weeks, I initially thought it was really complicated since there are a lot of commands that you need to understand. But when I started to contribute in our internal codebase, I (also with the help of my colleagues) managed to learn it along the way. I have lots of realizations on how this tool can benefit myself before and could’ve made me more productive back then, how I wish I learned about it sooner!

 to store our source code, keep track of our changes for every apps per microservices and run a CI/CD pipeline to ensure that stages are automated and done correctly.

Through git, we have a commit ID which keeps track of our changes. We can always go back to whichever version we have, in any cases that we wish to.

For example, imagine you are working on a feature within a webapp and made around six commits already. You tested the application, then found that there is a bug in your app. Upon checking, you realized that the bug was caused by the initial commit you had in the source code. With git, you can revert a change and undo it, so you can go back to the original state of the source code.

In our applications, we use GitLab as our git-based web platform environment for version control and source code management. Every day, there are changes to be made in the codebase. A developer can work concurrently with the other one through git since we utilize its branching functionality and it being a decentralized version control system. We create a branch for a specific update in our webapp, and merge it to the main ‘production’ source code once it’s finished. This way, we are not dependent on each other if we want to make changes to our applications. We can do things seamlessly without the worry of having any conflicts with what we are doing.

 is used as our DevOps platform. Since it is git-based, we use GitLab as our remote repository to store our source code. An event is triggered via git push/commit to the remote which will execute a CI/CD pipeline through GitLab. We use it to run automated build, test, and deployment. Through gitlab, we can deploy apps immediately without the need to wait for someone to finish something, since steps are already automated.

Every action we do within our internal repositories are tracked via webhook which is configured in a CI channel wherein relevant individuals are added. We use this as our collaboration method to notify and make everyone aware on what’s happening to our apps in microservices.

Using GitLab enables us to automate majority of version control and source code management. It’s beneficial for software engineering since manual steps are already taken out of the picture through its automated devops functionalities.

Imagine doing manual build, test, and deployment for your applications. You’ll go back to the days where it’s not as efficient as compared to the automated stages that a devops tool like GitLab can offer. For sure it must have brought a lot of headaches to every developer before! Lucky enough, we live in an advanced tech-era now where everything can be automated already.

. In layman’s term, this is the set of instructions that you need to configure to automate all the actions/stages that you’ll be automating in your application.

Someone just needs to understand how to write this CI file, and all the end-to-end stages for CI/CD will be automated already. That’s how handy and powerful this file is!

This is just the beginning of our code automation story. Just like git, this narrative can be further 

 to areas like application security, automated deployments, and gitops. But that would be for another blog! 😊

Note: This blog was originally published in August 2022 and has been updated for accuracy. 

Categories

DevSecOps

3 data decisions to make in 2024 for businesses to become AI-native

Minting JSON Web Tokens (JWTs)

Automating and securing code management with Git