On 22 February 2024, Human Managed and Microsoft Philippines hosted a joint roundtable session with our customers and partners to discuss operationalizing AI in enterprises' day to day processes, within their unique contexts. 

This article covers the key takeaways on the 3 'Ops' with the highest impact and value for businesses in the age of AI: DataOps (feeding AI), MLOps (tuning AI), and IntelOps (applying AI).

The progress in AI largely spearheaded by tech giants, open source communities and new players has made a new generation of AI-driven products and services accessible to any business that want them. Many are calling it the newest ‘inflection point’ in technology since cloud computing. Pilots, initial development and initial launch of AI apps for specific use cases have never been easier. But adoptable examples of operational AI with reusability, scalability and adaptability — especially in enterprise settings across multiple domains, is still limited.

We wanted to kickstart the conversation on what operationalizing AI in ASEAN businesses really looks like. It was an incredibly engaging session where we focused at length on the enterprise problems to which AI can be applied, particularly the “Day 2” operational ones that keep the business running and generate revenue. Thank you again to our customers and partners for being a part of this journey.

The first section of DataOps brought the focus to ground reality amidst the overwhelming hype and promises of AI — what working with data everyday to secure and scale a business is like.

I asked our speakers and guests to share openly about 

“What is the day to day data problem you have in operations?”

Their answers could be summarized into 5 buckets:

It’s difficult to know what you have and to track the changes in your business consistently. Without current and accurate view of your assets, their posture and behaviors, you are running blind.

Data outputs generated by tools and solutions are not always trustable or right. Human’s role is to analyze data critically, apply context and other perspectives. However, verifying data and removing false positives still remains largely manual, repetitive and time consuming.

Accurately predicting what is likely to happen from data analysis is desirable, but very challenging to achieve, because you need large volumes of reliable data, effective models, and quick feedback loops.

With data, events and alerts being generated everywhere, it’s hard to prioritize what is important, and to decide what to do at speed.

Bringing together all technologies and tools into cohesive operational pipelines to detect, react, and respond is difficult, especially with the volume and velocity of data keeps on increasing.

After establishing the ground reality from data perspective, we zoomed out to see what the world of opportunities and possibilities is from AI perspective.

, Data & AI Specialist from Microsoft shed light on the fast evolving landscape of AI, powered by huge advancements in foundation models including large language models. Businesses are spoiled for choices when it comes to plug-and-play AI tools and features that solves specific problems or tasks, such as document and meeting summaries, code generation, or natural language chatbot.

However, operating AI at scale across multiple use cases and business processes require custom-trained models. This is where businesses can potentially unlock competitive and differentiating value from AI, rather than using a ready-made solution that everyone else has access to. However, this is also where most MLOps and LLMOps challenges occur, such as:

with AI developments evolving so quickly, it is difficult to decide what direction to take and what models to use.

even after identifying the use cases and models for AI application, building the pipelines and productionizing requires deep technical knowledge, not to mention the capacity for experimentation (which will definitely come with failures).

typically in an enterprise, data generation and management is siloed, which leads to siloed and limited decisions. To achieve holistically contextualized intel from data, tracking the data lineage at its raw form is important to train the AI models but difficult to achieve, due to the data not being integrated.

Generic or foundation 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.

AI models only improve with proper feedback, so decisions have to be made as to who will give the feedbacks and the mechanism for it. There is also a responsibility to check for and reduce bias and prejudice.

No data to serving pipelines = no repeatable processes

Even when you make headway with the DataOps and MLOps problems, there is one big piece of the puzzle that is left in operationalized AI: presenting and serving the AI outputs to the right recipient (humans or machines) at the right time. What the outputs of dataops (insights & intel) and MLOps (labels) are in themselves important. What is also important — but often deprioritized — is how they get generated, and how they get delivered and tuned to be usable across the business.

At Human Managed, we call this continuous process of data-to-intel, intel-to-labels, labels-to-serving pipelines: IntelOps. To get the most out of AI in your day to day business processes, we believe it's crucial to build in the specifics of distribution of labor between humans and machines.

What Human or AI processes analyze what type of data and use cases? (Logic, model)

What Human or AI processes generate outputs (insights, recommendation)?

What Human or AI processes do you use to execute actions? (Functions, tasks)

Finally, how and where do you present the AI outputs? (API, report, notification)

We shared the ways that HM will be applying AI across all of our services. There is no one mode of AI; it will be different based on the type of implementation, function and the level of automation required.

Specific applications of AI depends on its implementation, function, and degree of automation.

Conclusion: The AI game is one of data, context, and processes

We cannot know what the future of AI holds, but one thing that is for certain is that there will be more data, not less. Everything is a datapoint that could be analyzed if you want it to be. But to what end? The human capacity and process improvements are not increasing at sufficient rate to keep up with the technological developments and expected outcomes, so we need to prioritize the data problems to solve.

Working with data is a given in today’s operations, but many enterprises are far from applying data ops at scale to contextualize and train AI models that is operational across key business processes.

Making data, models, and their outputs operationalized and always production-ready — not just once, but every day — with limited resources is the real challenge.

The good news is that it’s not that these problems are impossible to solve — in fact, many of them have been broken down and solved by different ecosystem players and domain experts, bringing new innovative products and services to the enterprise industry.

This shifts the technology service and partnership models as we know it, and makes data and AI platform a lot more accessible to enterprise customers than ever before. Just like how cloud computing distributed infrastructure and software, today’s AI developments are breaking down and distributing the AI functions (analytical, generative, model) even further.

The AI game is one of data, context, and processes. We believe that the companies that continuously build the context of their business as distributed and scalable data (instead of tribal knowledge in individual’s minds) and work with distributed ecosystem of partners and suppliers will be the ones that grow with AI instead of constantly playing catch up.

We all understand the potential of AI, but how are you applying it for your organization's operational processes, within its unique context?

Operationalizing AI: Human Managed & Microsoft Joint Roundtable on AI Strategy for ASEAN

[Singapore, Feb 14, 2024] – Human Managed, a leading cloud-native data platform, is proud to announce the launch of its Cyber Posture Management [CPM] Bundle. It is a comprehensive data-driven cyber operations solution designed to control cyber threats, vulnerabilities, weaknesses, violations, and reduce business risks. It does so by providing intelligence and recommendations for fast and actionable decision making.

A holistic data-driven security solution for enterprises

The Human Managed CPM Bundle automates fundamental data-driven services for cyber ops in enterprises. This holistic solution comprisesfour services necessary for organizational cyber readiness and maturity in today’s world flooded with data and tools: 

 for visibility and accuracy of the current state of digital assets, their relationships and their importance to the business

Cloud security posture management (CSPM) 

for speedy and accurate contextualization, prioritization of detections for effective cyber operations

 for continuous and accurate assessment of compliance

Simplified, flexible and timely security platform for building cyber resilience

Business leaders need to make quick decisions when they identify threats and be able to respond to attacks as fast as possible to ensure cyber resilience within their company. Currently, enterprises are getting swamped by managing multiple cyber security modules leading to information overload and indecision.

Karen Kim, CEO of Human Managed, said, “Human Managed is disrupting the market norm by making cybersecurity management more data-driven and holistic. We are launching the CPM Bundle to provide speed, simplicity, and flexibility for the customer with up to 25% cost savings. The CPM Bundle ensures all services are contextualized to the business automatically, with all intel, decision and recommendation in one interface. Customizable notification and dispatch workflows via any preferred channel (e.g. email, Slack, Teams, etc.) allow for timely notifications for decision makers.”

“There is no requirement for any additional procurement. Enterprises can either use existing investments in tools to act as data sources or activate the Human Managed platform’s 50+ out-of-the-box technology partners. There is no long-term contract lock-in, and the pricing model does not penalize based on data volume, as evident in most solutions available in the market. Try before you buy, with an optional monthly subscription.”, Karen Kim added.

Discovery to deployment customized for each business

The CPM Bundle is available to enterprises of all sizes. Service onboarding requires minimal effort from the customer organization and will be completed within two weeks. Deploying and configuring the CPM solution involves discovery, design workshops and implementation activities. Human Managed domain experts will work with customers to identify their monitoring requirements and objectives, ascertain their risk profile, and gather details about their environment and assets. These include understanding the business criticality of assets and existing security operations capabilities to determine the Data Collection Infrastructure requirements, posture use cases, and data sources required. A Service Blueprint is created, implemented, and tested before being handed over to the ServiceOps team for ongoing operations.

Current customers of Human Managed services in the essential services industries include [some of the largest] global banks, financial services institutions (clearing houses, payment systems), educational institutions in the ASEAN markets, including the Philippines and Cambodia.

Cyberattacks: no longer a matter of “if” but “when.”

With the average annual cost of cybercrime on the rise, expected to 

increase from $8.4trillion in 2022 to more than $23 trillion in 2027

, reports suggest that Asia Pacific is particularly vulnerable when compared to its global counterparts, accounting for 

31% of all incidents remediated worldwide

. Studies indicate that perpetrators are taking advantage of post-covid, digital adoption having outpaced digital literacy and cyber-awareness amongst users. It’s been estimated that t

he top 1000 companies in Southeast Asia are at risk of losing US $750 billion in market capitalization

 of the Human Managed CPM Bundle: www.humanmanaged.com/request-a-demo 

The Cyber Poster Management Bundle enables holistic, flexible and timely data-driven security for enterprises. 

Cyber Posture Management Bundle Release

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

A review of the year 2023 at Human Managed

2023, Processed

The Human Managed web app hm.works has been getting fresh updates regularly with intel, decisions, and actions for use cases to control threats, scale opportunities, and manage risks. 

This week, we are excited to announce the release of a dashboard that reports on the discovery and detection of exposed weaknesses in your internet-facing assets (cloud, SaaS, PaaS, IaaS, on-prem resources).  

Read more about our attack surface management use case

external attack surface management (ASM) 🔍⬅️

Personalized intel, ranked decisions & prescriptive actions from continuous and contextualized discovery and detection of your external-facing digital assets. 

The external attack surface management dashboard answers the top 3 things you need to know about your security exposure:

What is my external attack vector on my internet-facing assets? 

What exposure should I prioritize based on severity and exploitability?

What is the best action to reduce my external attack surface? 

The challenges of securing your attack surface

External attack surface is the point or vector through which an attacker could enter your environment through assets open to the Internet, such as websites, servers, cloud services, software, and more. 

While external ASM is nothing new, it is increasingly challenging to execute  effectively at scale, because of the abundance and frequent changes in your organization's digital make up.  As your digital initiatives and supply chain of service providers and vendors grow, so do the number of entry points your attackers could exploit. 

 of externally exposed assets and their weaknesses, and 

 to consistently reduce the attack vector. 

Some examples of personalized intel that help with external ASM are: 

known and unknown / managed and unmanaged assets 

vulnerabilities affecting your internet-facing assets 

SSL certificate expiry, validity / vulnerability 

dormant / unused digital assets (e.g. domains, subdomains, IP addresses)

HM's external attack surface management use case helps you to get to effective decisions and actions quickly, by taking 

Let's explore each segment of the external ASM dashboard. 

Insights about your attack vector and exposures in numbers. 

Insights about your attack vector and exposures in visual charts and narratives.  

Curated analysis and recommendations to manage your external attack surface from HM. 

 gives you customizable to key indicators (in this case, domains and subdomain status distribution) 

 gives you prioritized direction to reduce exposures, based on hm.works's analysis.

 gives you the steps to execute the recommended decision

 Helps you to understand your company's current external attack surface across all discovered and monitored internet-facing assets, and take prioritized decisions and actions to reduce exposures -- based on data.

Interactive grid with organized data on your external attack surface. 

 An interactive grid breaks down discovered and monitored public-facing assets' exposure based on:

 (domain, SSL certificate, IP address, website, social account, technology )

(e.g. active / dormant subdomain, cloud IP address, third party library, network app)

 Understand and explore your current state of external exposure in detail, identify blockers, and plan for improvements to achieve desired posture goals. 

Drill down of all available details on an asset and its discovery path 

And that is hm.works 1.17! We will be releasing more features and updates regularly, so stay tuned.

To get the latest news from Human Managed, follow us on LinkedIn and check out our website and blog.

Want to discuss how we can help solve your cyber, digital, or risk operations through data?

Please contact us at hello@humanmanaged.com.

Human Managed app hm.works 1.17 released with dashboard on detected exposures & prioritized actions

Human Managed hm.works 1.17 released with external attack surface management

Human Managed app hm.works 1.16 released with dashboard on detected vulnerabilities & prioritized mitigation and remediation 

Since its first release on 13 March 2023, the Human Managed web app hm.works has been getting fresh updates regularly to report on intel, decisions, and actions generated from any data source from our customers.

This week, we are excited to announce the release of a dashboard that reports on the detection of vulnerabilities on managed assets (servers and infrastructure components hosted in corporate network / datacentres / cloud, or apps hosted in public app stores).  Read more about our 

vulnerability use case on our newly revamped website

Intelligence, recommended decisions and actions from continuous and integrated vulnerability assessment across your managed assets. 

The vulnerability dashboard answers the top 3 things you need to know about enterprise security:

What critical vulnerabilities exist in my environment? 

What vulnerabilities should I prioritize based on exploitability?

What is the best action to mitigate or remediate the vulnerability?

The challenges of vulnerability management

Vulnerability is a flaw or weakness in a system that can be exploited to compromise IT security. It can be caused by weakness in any IT administration, process, or design function, such as: 

insecure products & app from system and app vendors 

flaws in user administration / system access 

Effective vulnerability management (from discovery to remediation) requires a proactive, integrated approach that enables continuous vulnerability assessments, prioritized response based on business impact, and effective planning & tracking of remediation actions. 

Most organizations recognize the importance of vulnerability management, but struggle to: 

get accurate visibility of their network topology 

get understanding of security & availability implications of all network devices & computer systems (which can easily be >1000s) 

effectively plan network changes & maintain security controls 

contextualize vulnerabilities based on exploitability / exposure / impact

prioritize urgent and critical issues from data overload on a day to day basis 


HM's vulnerability management use case helps you to get to effective decisions and actions quickly, by taking 

 to vulnerability management -- regardless of the source of threat, or the environment in which a vulnerability is located. 

Let's explore each segment of the posture violation dashboard.

 gives you a high level summary of all open vulnerabilities detected in your assets, customizable to key indicators (in this case, detection severity)

 gives you a direction to prioritize to fix vulnerabilities, based on hm.works's analysis.

 Helps you to understand your company's current state of vulnerabilities across all managed assets, and take prioritized decisions and actions to improve the posture -- based on data.

Connect data sources such as vulnerability management, endpoint management, and network management to the Human Managed platform to perform contextualized analysis on vulnerability use cases.

 An interactive grid breaks down detected vulnerabilities based on:

 (common vulnerabilities and exposures identifier for publicly known information security vulnerabilities) 

(common vulnerability scoring system for quantifiable and standard measure of severity of vulnerabilities) 

 (quantifiable and standard measure of impact of vulnerabilities) 

(common weakness enumeration for community-developed list of software and hardware weakness types) 

 (e.g. network management platform, cloud security posture management, vulnerability scanner) 

CPE name, CPE version, app engine, app engine version 

 Understand and explore your current state of vulnerabilities in detail, identify blockers, and plan for improvements to achieve desired posture goals.

 Connect data sources such as vulnerability management, endpoint management, and network management to the Human Managed platform to perform contextualized analysis on vulnerability use cases.

And that is hm.works 1.16! We will be releasing more features and updates regularly, so stay tuned.

To get the latest news from Human Managed, follow us on LinkedIn and check out our website and blog. 

Human Managed hm.works 1.16 released with detection and resolution of vulnerabilities in your assets

Human Managed hm.works 1.16 released with continuous vulnerability management

Since its first release on 13 March 2023, the Human Managed web app hm.works has been getting fresh updates every week or two to report on intel generated from any data source from our customers.

Recently, we've had a series of releases on use cases to improve overall cyber posture of an organization: 

v.15 for ISO 27001:2022, SOC 2, NIST And CIS Benchmark compliance 

Today we are excited to release further updates on v1.15 for you to not only consume intel about posture violation and compliance, but to 

 a drill-down page from every cloud config violation, its check, the check's rationale and remediation steps

 allows you to understand the violation's impact across all monitored assets and mitigate prioritized violation issues on the spot 

(cloud resources under AWS accounts, Azure subscriptions, and GCP projects)

 to build, configure, and monitor cloud compliance use cases on the hm.works platform.

Here is the intel to decision to action flow: 

on top of every use case page to inform your next decision and action. 

Categories

Recent

Operationalizing AI: Human Managed & Microsoft Joint Roundtable on AI Strategy for ASEAN

Cyber Posture Management Bundle Release

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

2023, Processed

Human Managed hm.works 1.17 released with external attack surface management

Human Managed hm.works 1.16 released with continuous vulnerability management

Human Managed hm.works 1.15.2 updated with remediation for CIS policy violations

Human Managed hm.works 1.15.1 updated with scorecards for cloud compliance and configs

Human Managed hm.works 1.15 released with ISO 27001:2022, SOC 2, NIST and CIS benchmark compliance for cloud