Monthly Archives: April 2021

Champ SuthipongchaiContributor

With the United States moving all-in on massive infrastructure investment, much of the discussion has focused on jobs and building new green industries for the 21st century. While the Biden administrations plan will certainly expand the workforce, it also provides a massive opportunity for the adoption of automation technologies within the construction industry.

Despite the common narrative of automating away human jobs, the two are not nearly as much in conflict, especially with new investments creating space for new roles and work. In fact, one of the greatest problems facing the construction industry remains a lack of labor, making automation a necessity for moving forward with these ambitious projects.

The residential construction industry alone had some 223,000 and 332,000 unfilled construction job vacancies at the peak unemployment rate of 15% in 2020, but thats actually about the same when unemployment was only at 4.1%. Between 1985 and 2015, the average age of construction workers increased from 36 to 42.5, while those aged 55 and older increased from 12% to over 20%. The 2018 Population Survey conducted by the Census Bureau found that workers under 25 comprised just 9% of the construction industry, compared to 12.3% of the overall U.S. labor force.

Productivity in the construction industry has likewise remained static since 1995, primarily driven by the aging demographic of the existing labor force, the apprenticeship nature of the job, and difficulty in attracting and retaining new workers. In short, there is insufficient labor to do the job, while existing staff are becoming increasingly less productive as skilled workers that have accumulated decades of experience in their crafts are lost due to retirement.

Automation will need to be a key element of any major infrastructure push, especially if we hope to meet the ambitious goals of current proposals. That being said, not all areas of the construction industry are primed, or even viable, for this shift to automation.

Construction is one of the worlds largest industries but has two major challenges: market fragmentation and complex stakeholders.

The construction industry as a whole is nationally fragmented but occasionally locally concentrated. This differs depending on the segment and type of construction company, with each generally comprising less than 10 workers. The top 100 general contractors account for less than 20% of the total construction market. Subcontractors are even more fragmented, with top players accounting for less than 1% of the total market share. This makes sales processes and scaling very slow and highly inefficient.

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Investment in construction automation is essential to rebuilding US infrastructure - TechCrunch

As industries try to play catch-up as COVID-19 pushed everyone to remote work, technical debt is more of an issue than ever before. Technical debt is a term that describes the implied cost of additional work, or re-work, caused by choosing an easy solution over a better solution that may take longer to implement. Like financial debt, if technical debt isnt dealt with or repaid, it can accumulate interest, making it more difficult for companies to implement sweeping changes that could improve the organization or improve the software.

As artificial intelligence (AI) and automation become more pervasive in the modern world, one of the areas they focus on is this technical debt that organizations have mired themselves. Without AI solutions, the debt will build and build, making software more bloated and more modular the longer it is in effect. The solution lies in automation where the technical debt can be addressed and further improve an organizations cyber resiliency and where return on security investment (RoSI) can be calculated.

Automation is especially appealing to those in cybersecurity when looking at GRC solutions that are modular and siloed and have difficulty talking to one another. This also contributes to technical debt and requires human intervention as it accumulates. For many organizations, execution, governance, and operational success are elusive because there have not been the same kind of breakthroughs for most cybersecurity practices compared to other industries that use AI, and the awareness of automation options remains low.

According to Gartner though thats changing, 30% of enterprises plan to increase AI investments since the start of the pandemic, and 47% of AI investments remain unchanged. The ability for a company to automate also depends on their maturity level. Companies must have a sufficiently mature security program in order to take advantage of automation opportunities. If automation was implemented on a larger scale, security incidents, financial loss, data loss, and data breaches could be mitigated more effectively.

Automation is slowly but surely transforming from an optional condition to a means of survival. Although there was a demand for it pre-pandemic, the global crisis has accelerated digital transformation initiatives and broken down some of the obstacles higher-level executives had with automation processes.

AI encompasses a vast range, including machine learning (ML), natural language processing (NLP), Robotic process automation (RPA), and more. NLP especially plays a significant part in advanced automation. NLPs ultimate objective is to read, decipher, and understand language thats valuable to the end-user. Currently, there are several ways NLP is used in day-to-day life. Many are familiar with chatbots or auto-complete in emails or texts. But theres a gap in cybersecurity and risk assessment where NLP could be used to inform risk management and regulatory compliance. Since interactions between humans and machines are based on language processing, NLP allows organizations to process increasingly large amounts of data, granting them the ability to be more efficient, more risk cognizant, and more secure.

Incident detection and prediction is one area where humans can take advantage of AI. NLP used for risk and compliance requirements can identify overlaps in frameworks and data from an enterprises tech stack and use it to identify vulnerabilities in security infrastructure.

However, there are a number of AI-fueled cyber security solutions out there, and it has definitely become a buzzword in cyber. These GRC automation tools still tend to require human oversight and intervention. They do not achieve pure automation. In this industry, automation tends to encompass employees and security leaders getting texts or emails when controls need to be addressed or updated, but what if there were more powerful options that allowed for an automated system that could identify how threats endanger your current tech stack?

At an operational level, advanced automation addresses risk and the plague of technical debt that business leaders and IT face. Automation allows executives to allocate resources in a way that will yield the greatest return on information security investment and gives them the opportunity to augment as many processes and systems as possible to increase resilience, efficiency, and agility. Doing away with siloed data and technical debt that cripples monitoring strategy allows security teams to increase their maturity and allocate resources to automation options that give a significant return on investment and to calculate the return.

Advanced automation is critical in addressing modern security challenges by increasing speed and efficiency while cutting back on operational costs. Solving the pent-up data debt frees up vital resources and employees to focus on other matters and reduces inefficiency.

By combining AI and NLP, security leaders can make sense of data coming out of a security tech stack, showing where and how various tools and solutions manage compliance programs across standards. NLP allows for improvements over time with self-learning to become more efficient in enhancing cybersecurity processes. The automation of assessments gives business leaders insight into real-time risk monitoring.

Crosswalking is a process where the NLP engine identifies keywords that map to specific controls and control actions. Currently, the process of crosswalking in many cybersecurity solutions is manual and inexact. NLP gives organizations the ability to leverage nascent data thats coming out of a platform. When other cybersecurity companies discuss crosswalking its typically behind a closed door, and no one knows how it happens or what it does. Mapping different frameworks dont always provide a direct 1:1 solution. So having an option for automation that is transparent, thorough, and learns, is critical in increasing maturity and understanding.

CyberSaint takes a deep learning approach that allows crosswalking between frameworks without a significant amount of human intervention. This increases security maturity and makes the organization more risk cognizant. This becomes even more key when discussing cloud-based shifts post COVID-19.Many Fortune 500 companies use spreadsheets to track and monitor risks and vulnerabilities, but this incredibly time-consuming task, typically done by an employee manually checking controls. By the time IT professionals reach the end of the spreadsheet, it can already be out of date. This leaves organizations vulnerable and consumes valuable resources that could be allocated elsewhere. In the era of COVID-19, with many companies downsizing their security budgets, this can be a massive blow to a cybersecurity program as resources become scarcer and employees gain an ever-growing list of responsibilities.

With automated risk platforms, its possible to manage risks and increase program maturity over time, dynamically. Software that can continuously prioritize threats and add more automation over time across compliance, risk, and audit can make a sizeable difference in downsized security teams. It becomes possible to track tangible, measured impacts and returns on investment with a balance between quantitative and qualitative data.

Executives need to start demanding more and expecting more of their IT GRC stacks. To achieve operational excellence, they need to shed the technical debt and move forward with automation to augment current processes and make them perform for them instead of constantly utilizing workarounds that are inefficient and leave critical systems open to risk.

To learn more about risk management and how automation is changing the landscape of cybersecurity, click here. To see a demonstration of CyberStrong and how it can help your enterprise to more with less, contact us.

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How Advanced Automation and New Technologies are Enabling Enterprises to Do More with Less - Security Boulevard

Demand for digital automation skills has more than doubled since 2019, according to new research from specialised recruiter Robert Half and global labour market trends experts Burning Glass. The need for automation proficiency is rising faster in non-technical roles, up by 125% since 2019, than it is in traditional IT roles, which are up by 96%. Non-technical roles seeing strong growth in demand include Management Consultants and Sales Directors.

New research from specialist recruiter Robert Half and global labour market trends experts Burning Glass indicates that demand for digital automation skills is set to double (+99%) by the end of 2021 compared to pre-pandemic figures. An analysis of nearly 9,000,000 UK job postings has revealed that the number of jobs requiring automation capabilities is on course to reach around 90,000 this year, compared to 39,323 in 2019, with demand rising fastest in non-technical roles such as Management Consultants and Sales Directors.

Three strategies to help integrate automation into the workforce:

1. Find the quick wins: Look for opportunities to roll out easy proof of concept (PoC) trials or pilot programmes so that stakeholders across the business can see how such projects can work and learn how to go about introducing automation. PoC trials shouldn't be hugely ambitious and therefore don't necessarily require large amounts of technical knowledge to get started.

2. Do a cost / benefit analysis: For each project, it's essential for business leaders to understand how implementing an automated solution will affect the team, what the expected benefits are, and how much resources it will require. This helps to create a strategic approach to automation where the highest value opportunities are given priority.

3. Think about the broader impacts: More broadly, apart from productivity and efficiency considerations, executives should consider conducting an impact analysis of the effects of automation on things like employee happiness, career development, and training and development so that they can anticipate and pre-emptively address these issues.

"As automation becomes more widespread, roles and job functions will change significantly," continued Weston. "This surge in demand means it's more important than ever for businesses to invest in life-long learning. It's estimated that 21m UK workers will need digital upskilling over the next decade and automation needs to be a core element of such educational initiatives. As such, executives should be reviewing their learning and development programmes to make sure they are able to provide the skills most urgently needed. Alongside this, all businesses - especially SMEs - need to be taking advantage of government support to help with digital upskilling such as the Chancellors recent Help to Grow' scheme that can help shoulder some of the cost of upskilling and retraining."

See more here:

UK demand for digital automation skills doubles as employees feel the burn out | RoboticsTomorrow - Robotics Tomorrow

Taking a proactive approach to threat hunting in cyber security is crucial, especially today when attacks are more stealthy and more complex than ever. What this means is that the olden ways of cyber security relying on time-consuming manual workflows are slowly becoming obsolete, and cyber security teams must be supported by active learning intelligence in their threat hunting processes.

This is why, in order to respond to these pressing needs, DFLabs crafted Supervised Active Intelligence, which is a combination of multiple capabilities, all working together to ensure a smooth and interrupted SecOps workflow. In this article, we will describe the power of Supervised Active Intelligence SAI, reveal its role in the cyber security environment, and talk about the potential of active intelligence in cyber security.

Supervised Active Intelligence provides analysts with all the information they need to make well-informed decisions. It is a special case of machine learning which rests upon IncMan SOARs learning algorithm to query a user and extract relevant data. Its supervised learning algorithm leans on machine learning to:

Via Supervised Active Intelligence, SOC teams can successfully decide to orchestrate, contain, report, and remediate breaches with fewer resources required and in a much quicker and more efficient manner.

Supervised Active Intelligence was built to elevate the maturity of automation in security operations, and help build a trusting relationship between humans and SOAR by allowing security professionals to take a crucial decision-making role in the path of automation.

We said several times that SOAR does not replace humans but makes them more efficient. Many doubts are raised about the development of advanced cyber security technologies, mainly because it is feared that forward-thinking tools will eventually render humans obsolete. However, that is not the fact, nor it will be in the foreseeable future.

The way Supervised Active Intelligence operates is that it relies on human guidance to launch machine learning and automation initiatives. This is the principle upon which SAI was built in IncMan SOAR. SAI follows a guided path to automation led by security professionals. Its functionality is strictly dependent on human guidance, which is the foundation of the relationship between security teams and Supervised Active Intelligence.

Supervised Active Intelligence was built to help security teams accomplish their desired targets with enhanced efficiency, speed, and performance. SAI does not, however, work autonomously.

The key element to remember here is that security teams supervise the active intelligence brought by IncMan SOAR. SAI will only launch machine learning processes after it has been instructed by the security professionals responsible for the given case. After receiving the instructions, SAI will continue on its automation journey, but it is vital to remember that ACTIVE does not mean AUTONOMOUS in this situation. SAI is and will be dependent on human guidance.

Supervised Active Intelligence in IncMan SOAR is based on the premise of human guidance. It fuses machine learning and human intelligence to formulate the end result Supervised Active Intelligence.

More specifically, Supervised Active Intelligence is driven by the following:

This is, in short, the basis behind the design and development of Supervised Active Intelligence in IncMan SOAR. Its role is to bring a new, more effective path to security automation that will offer a higher level of efficiency and success in incident remediation.

In other words, SAI will help humans be more effective in SecOps.

SAI speeds up the decision-making process for security analysts by providing them with applicable recommendations regarding a given incident. Upon providing relevant Playbooks to a certain incident based on historical precedent, SAI uses its machine learning capabilities to eliminate the false positives and create incidents when they are real and extract more intelligence relevant to the incident, ultimately helping the security professional make a fast, well-informed decision.

This saves security professionals from the time-consuming hassle of having to search for relevant information regarding an incident on various tools. Heres exactly how SAI gives humans the much-needed edge in security operations:

Phase #1:

Eliminating false positives via TRIAGE capabilities and creating incidents that are real. SAI supports analysts with its TRIAGE capabilities in order to eliminate false positives and create incidents when they are real. To help you accomplish that, our team helps you set incident rules that will enable the SAI engine to understand what qualifies as a false positive and what falls into the category of incidents.

Phase #2

Allowing analysts to make the right decision by recommending relevant Playbooks. SAI supports analysts by allowing them to make well-informed decisions. The Supervised Active Intelligence engine recommends the right Playbooks and uses its machine-learning algorithm to find the most suitable way to respond to the incident. It Enriches the team with the right information to help them launch an immediate response to the incident.

Via Supervised Active Intelligence, CISOs and SOC Managers will have a great way to incorporate machine learning into their security operations without disrupting the existing workflow within their SOC team.

All in all, there is literally no downside in incorporating Supervised Active Intelligence into your conventional SecOps. Your analysts will still be behind the scenes controlling the wheel, with the only difference being that SAI will perform all the time-consuming and repetitive tasks for them.

Bottom line is, security leaders are trying to find new ways to make automation in security operations more effective. Humans are yet to give automation their full trust, but with SAI, automation in cyber security is taking a great forward.

Automation is deemed as a core pillar in the heart of security automation, along with machine learning and active intelligence. And by refining the development of security automation, humans will get a much-needed helping hand that will boost the efficiency, speed, and accuracy of their security operations. Learn more about the other powers of IncMan SOAR and the bountiful benefits of progressive automation.

Larticolo Supervised Active Intelligence The Next Level of Security Automation proviene da DFLabs.

*** This is a Security Bloggers Network syndicated blog from Our Blog DFLabs authored by DFLabs. Read the original post at: https://www.dflabs.com/resources/blog/supervised-active-intelligence-the-next-level-of-security-automation/

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Supervised Active Intelligence The Next Level of Security Automation - Security Boulevard

Demand for digital automation skills is rising in the UK as businesses try to find ways to support workers by removing repetitive tasks, according to research.

The need for automation proficiency is accelerating faster in non-technical roles such as management consultants and sales directors, up by 125% since 2019, than in traditional IT roles, which are up by 96%, according to a survey carried out by specialist recruiter Robert Half and global labour market trends expert Burning Glass.

According to the research, which analysed nine million UK job postings, the number of roles requiring automation capabilities is set to reach around 90,000 this year, compared with 39,323 in 2019.

These findings show that businesses understand they need to find ways to support and supplement their workers by removing more repetitive tasks and freeing them up to focus on more value-add activity, said Matt Weston, managing director at Robert Half UK.

Sales directors are increasingly being tasked with improving efficiency, and are hence turning to automation, including software that auto-fills client contact forms during phone calls or using artificial intelligence to support on-the-job training, according to Weston.

Executives should be reviewing their learning and development programmes to make sure they are able to provide the skills most urgently needed Matt Weston, Robert Half UK

As automation becomes more pervasive in the workplace, roles and job functions will change significantly, he said. Citing numbers from the Confederation of British Industry (CBI) that about 21 million UK workers will need digital upskilling in the coming decade with automation as a key area of focus, the executive argued that this ongoing change also meant it was important for businesses to invest in continuous training.

Executives should be reviewing their learning and development programmes to make sure they are able to provide the skills most urgently needed, said Weston.

In addition, the Robert Half executive noted that businesses, particularly small and medium-sized enterprises (SMEs), should take advantage of government support to help with digital upskilling, such as the Help to Grow scheme announced by chancellor Rish Sunak in this years Budget.

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UK demand for digital automation skills soars - ComputerWeekly.com