Tag Archives: shared

WHAT ARE SOME KEY FACTORS TO CONSIDER WHEN ASSESSING THE FEASIBILITY OF CREATING AN HR SHARED SERVICES CENTER?

Leave a reply

Cost Savings and Economies of Scale

One of the primary goals of establishing an HR shared services center is to reduce costs through economies of scale. By consolidating common HR transactional processes like benefits administration, payroll processing, recruitment, etc. across different business units or legal entities, there are opportunities to reduce overhead costs. A larger centralized team can handle the volume of work more efficiently compared to having these functions spread out in each business unit. Standardizing systems, processes and policies further drives efficiencies. Detailed cost-benefit analysis considering factors like staffing requirements, technology investments required, expected transaction volumes etc. would need to be done to evaluate potential cost savings.

Process Standardization

For a shared services model to be effective, it is important that the HR processes handled by the center are standardized. Key transactional processes should be harmonized with common workflows, documents, approvals etc. across all client groups. This allows the centralized team to handle the work in a streamlined, uniform manner gaining maximum benefits of consolidation. Assessing the level of standardization currently existing across different HR functions, client groups and geographies is important. The effort required to standardize legacy disparate systems, policies etc. should also be considered in feasibility evaluation.

Scope of Services

Defining the appropriate scope of services that would be handled by the HR shared services center is a critical factor. The scope could range from basic transactional services like data entry, time & attendance, payroll processing to more strategic services like HR analytics, talent acquisition etc. Feasibility would depend on factors like the capabilities required in the shared services team, investment needs, expected ROI, impact on the organizations etc. An optimal balance needs to be struck between scope of services and business case.

Client Onboarding and Transition

Transitioning the HR responsibilities and employees (if any) of client groups to the shared services model requires detailed planning. Engaging clients, communicating changes, transitioning data and processes, HR employee relations, training client SPOCs are some aspects to consider. A phased transition approach may be required. Client acceptance, readiness and cooperation are important to the success and sustainability of the shared services model. Resistance to change could impact feasibility.

Technology Enablement

Effective HR shared services is heavily reliant on enabling technologies like ERP systems, workflow automation tools, case management systems, portals, reporting solutions etc. The complexity and cost of implementing and integrating these technologies need to be evaluated. Existing systems landscape across client groups, compatibility, data migration needs are factors in assessing technology requirements and feasibility.

Governance Structure

Developing a robust governance structure which clearly defines roles of the shared services entity vs client groups is important. Aspects like decision rights, SLA frameworks, dispute resolution mechanisms, review mechanisms need clarity upfront. Governance defines accountability which impacts sustainability. Governance design should balance efficiency gains with client experience and control considerations.

Regulatory and Compliance Needs

Shared services center operations need to adhere to various employment, payroll, data privacy, and other applicable compliance regulations across jurisdictions. Performing due diligence on regulatory landscapes for all in-scope geographies and functions becomes important from a feasibility perspective. Addressing compliance needs can impact timelines, efforts and costs significantly.

Resourcing and Talent Availability

A reliable source of requisite skills and capabilities is needed at the shared services location. Factors like availability of labor pools with appropriate HR generalist, domain and technology skills, language abilities, scalability need assessment form part of feasibility evaluation. Attrition risk over the long term also needs consideration while resourcing the shared services center.

Location Strategy

Selecting the right location(s) for establishing shared services center(s) is a strategic decision impacting costs, proximity to clients, access to talent, business continuity etc. A thorough analysis of location options based on primary selection criteria allows data-driven decisions on location strategy and feasibility

Change Management Planning

A robust change management strategy is critical to successful establishment and sustainability of shared services model. Aspects like stakeholder engagement, communications approach, organizational readiness assessment, change impacts on clients and internal teams need detailed planning. Change management implementation timeline, costs are factors in feasibility review.

Carefully evaluating the key factors listed above through a cross-functional, data-driven feasibility study approach allows for an objective assessment of opportunities, risks and overall viability of the HR shared services center concept. A favorable feasibility would set the foundation for a successful shared services transformation initiative.

CAN YOU PROVIDE MORE INFORMATION ON THE SHARED RESPONSIBILITY MODEL IN CLOUD SECURITY

Leave a reply

The shared responsibility model is a core concept in cloud security that outlines the division of responsibilities between cloud service providers and their customers. At a high level, this model suggests that cloud providers are responsible for security “of” the cloud, while customers are responsible for security “in” the cloud. The details of this model vary depending on the cloud service model and deployment model being used.

Infrastructure as a Service (IaaS) is considered the cloud service model where customers have the most responsibility. With IaaS, the cloud provider is responsible for securing the physical and environmental infrastructure that run the virtualized computing resources such as servers, storage, and networking. This includes the physical security of data centers, server, storage, and network device protection, continuous monitoring and vulnerability management of the hypervisor and operating systems.

The customer takes responsibility for everything abstracted above the hypervisor including guest operating systems, network configuration and firewall rules, encryption of data, security patching, identity and access management controls for their virtual servers and applications. Customers are also responsible for any data stored on their virtual disks or uploaded into object storage services. Data security while in transit also lies with the customer in most IaaS models.

Platform as a Service (PaaS) splits responsibilities differently as the provider now takes care of more layers including the OS and underlying infrastructure. With PaaS, the provider secures the operating system, hardware, storage and networking components. Customers are now responsible for securing their applications, data, identity controls, vulnerability management, penetration testing and configuration reviews for their applications. Responsibility for patching the runtime environment remains with the provider in most cases.

With Software as a Service (SaaS), the provider takes on the most responsibility securing the entire stack from the network and infrastructure to the operating system, software, application security controls and identity access management. Customers only bear responsibility for their data within the application and user access controls. Security of the application itself is entirely handled by the provider.

The deployment model being used along with the service model further refines the split of duties. Public cloud has the most clearly defined split where the provider and customer are distinct entities. Private cloud shifts some responsibilities to the cloud customer as they have greater administrative access. Hybrid and multi-cloud complicate assignments as workloads can span different providers and deployment types.

Some key responsibilities that typically fall under cloud providers across models include secure host environment configuration; infrastructure vulnerability management; system health and performance monitoring; logging and auditing access to networks, systems and applications; disaster recovery and business continuity; physical security of data centers; hardware maintenance and patching of system software.

Customers usually take lead in areas like encryption of data-at-rest and data-in-transit; authentication and authorization infrastructure for users, applications and services; vulnerability management of their workload software like databases and frameworks; configuration management and security hardening of virtual machines; adherence to security compliance regulations applicable to their industry and data classification levels; managing application access controls, input validation and privileges; incident response in coordination with providers.

Sharing responsibility effectively requires close cooperation and transparency between providers and customers. Customers need insights into provider security controls and oversight for assurance. Likewise, providers need informed participation from customers to secure workloads effectively and remediate issues in a shared environment. Security responsibilities are never completely moved but cooperation to secure respective domains enables stronger security for both parties in the cloud.

The takeaway is that the shared responsibility model allocates security duties in a clear but dynamic manner based on factors like deployment, service and in some cases operating models. It provides an overarching framework for defining security accountabilities but requires collaboration across the whole stack to achieve security in the cloud holistically.

HOW CAN AI BE DEVELOPED AND APPLIED RESPONSIBLY TO ENSURE ITS BENEFITS ARE SHARED BY ALL

Leave a reply

There are several critical steps that can help ensure AI is developed and applied responsibly for the benefit of all humanity. The first is to develop AI systems using an interdisciplinary, transparent, and accountable approach. When developing advanced technologies, it is crucial to bring together experts from a wide range of fields including computer science, engineering, ethics, law, public policy, psychology, and more. Diverse perspectives are needed to consider how systems may impact various communities and address potential issues proactively.

Transparency is also vital for building trust in AI and accountability into the process. Researchers and companies should openly discuss how systems work, potential risks and limitations, design tradeoffs that were made, and allow for external review. They should also implement thorough testing and evaluation to verify systems behave as intended, don’t unfairly discriminate against or disadvantage groups, and are robust and secure. Establishing multistakeholder advisory boards including outside advocates can help provide oversight.

To ensure the benefits of AI are shared equitably, its applications must be developed with inclusion in mind from the start. This means collecting diverse, representative data and validating that systems perform well across different demographic groups and contexts. It also means designing interfaces, services and assistance that are accessible and usable by all potential users regardless of ability, economic status, education level or other factors. Special attention should be paid to historically marginalized communities.

Where possible, AI systems and the data used to train them should aim to benefit society as a whole, not just maximize profit for companies. For example, healthcare AI could help expand access to medical services in underserved rural and remote areas. Educational AI could help address resource inequities between well-funded and low-income school districts. Assistive AI applications could empower and enhance the lives of people with disabilities. Public-private partnerships may help align commercial and social goals.

As AI capabilities advance, job disruption is inevitable. With proactive policies and investment in worker retraining, many new job opportunities can also be created that require human skills and judgment that AI cannot replace. Governments, companies and educational institutions must work cooperatively to help workers transition into growing sectors and equip the workforce with skills for the future, like critical thinking, problem solving, digital literacy, and the ability to work collaboratively with machines. Universal basic income programs may also help address economic insecurity during substantial labor market changes.

AI policy frameworks, regulations and standards developed by stakeholders from industry, academia, civil society and government can help guide its development and application. These should aim to protect basic rights and values like privacy, agency, non-discrimination and human welfare, while also supporting innovation. Areas like algorithmic accountability, data governance, safety and security are important to consider. Policymakers must delicately balance oversight with flexibility so regulations don’t become barriers to beneficial progress or spur development elsewhere without protections.

Internationally, cooperation will be needed to align on these issues and ensure AI’s benefits flow freely across borders. While cultural viewpoints on certain technologies may differ, core concepts like human rights, environmental protection and equitable access to resources provide common ground. Open collaboration on benchmarks, best practices, incident reporting and response could help countries at varying levels of development leapfrog to more responsible implementation. Global partnerships may also foster the types of highly skilled, diverse workforces required to develop responsible AI worldwide.

With a conscious, coordinated effort by all involved—researchers, companies, civil society, governments, international organizations and individuals—artificial intelligence has immense potential to help solve humanity’s grand challenges and leave no one behind in an increasingly digital world. By following principles of transparency, inclusion, accountability, and aligning technological progress with ethical and social priorities, we can work to ensure AI’s many benefits are developed and shared responsibly by all people. Ongoing vigilance and adaptation will still be needed, but taking proactive steps now increases the chances of building a future with AI that works for human well-being.