Tag Archives: organizations

HOW CAN ORGANIZATIONS IMPROVE THEIR INCIDENT RESPONSE CAPABILITIES?

Leave a reply

Establish an Incident Response Team: One of the most important steps is to establish a dedicated incident response team. This can be a full-time team or an on-call team that can be activated when needed. The team should comprise of members from different departments like IT, security, legal, HR, PR etc. Having a pre-defined incident response team ensures that the organization is ready to respond quickly in case of any security incidents.

Develop an Incident Response Plan: The incident response team should develop a detailed incident response plan catered to the specific needs and risks of the organization. The plan should document the incident handling procedures, roles and responsibilities of team members, communication protocols, escalation procedures and strategies to deal with different types of incidents. Regularly testing and updating the plan is necessary to keep it effective.

Conduct Tabletop Exercises: Tabletop exercises involve bringing the incident response team together to walk through different hypothetical incident scenarios. This helps evaluate the team’s preparedness and the incident response plan. Issues noticed during the exercises should be documented and the plan updated. Regular exercises test and refine the coordination between team members and processes.

Implement Monitoring and Detection Controls: Organizations must implement technical controls to facilitate early detection and monitoring of incidents. This includes deployment of tools like SIEM, firewalls, network monitoring systems etc. to continuously monitor the IT infrastructure for anomalies, threats and signs of compromise. Early detection is crucial for reducing impact of incidents.

Establish Response Processes: Clear processes need to be defined for handling incidents once detected. This includes initial response and containment procedures, further investigation, evidence collection, impact assessment, recovery and lessons learned. Failover and backup infrastructure should be in place to minimize business disruptions. Processes ensure methodology and consistency in incident handling.

Conduct Training and Awareness: Regular security awareness training programs help employees understand cyber threats and report any suspicious activities promptly. Incident response training keeps the response team updated on the latest tools, strategies and best practices. Mock training scenarios test the coordination and preparedness of team members in implementing response plans and processes. This shapes an incident-ready culture across the organization.

Engage with External Stakeholders: Depending on the incident, external expertise may be required from forensic investigators, law enforcement, PR agencies etc. Maintaining relationships with trusted partners through regular interactions ensures their timely assistance when needed. Sharing and collecting threat information also helps gain broader intelligence to further strengthen defenses.

Perform After-Action Reviews: Post-incident evaluations are important to identify gaps, document learnings and further improve readiness. Key questions around effectiveness of response, timeline, coordination, communication, impact assessment and ways to enhance overall maturity of the program in handling future threats need to be reviewed. Addressing issues brings continuous enhancement to the incident response capabilities.

Develop a Communication Strategy: A well-defined internal and external communication strategy is critical to keep appropriate stakeholders informed during and after an incident. This mitigates potential impacts through timely sharing of accurate information while avoiding regulatory or legal issues. The legal and PR team should help create policies and processes around information dissemination.

Budget and Resource Allocation: Ensuring appropriate budget allocation to account for advanced tools, training, third party services and upgrading infrastructure when needed strengthens effectiveness. Management commitment through dedicated annual budget planning facilitates long term maturity enhancement of the incident response program.

The above measures establish a robust foundation and processes to comprehensively handle security incidents. Regular practice, reviews and improvements further institutionalize incident response as part of the overall security strategy and operations of an organization. A mature capability creates preparedness to effectively deal with threats and reduce risks to business operations and reputation.

WHAT ARE SOME CHALLENGES THAT ORGANIZATIONS MAY FACE WHEN IMPLEMENTING AI AND MACHINE LEARNING IN THEIR SUPPLY CHAIN

Leave a reply

Lack of Data: One of the biggest challenges is a lack of high-quality, labeled data needed to train machine learning models. Supply chain data can come from many disparate sources like ERP systems, transportation APIs, IoT sensors etc. Integration and normalization of this multi-structured data is a significant effort. The data also needs to be cleaned, pre-processed and labeled to make it suitable for modeling. This data engineering work requires skills that many organizations lack.

Model Interpretability: Most machine learning models like deep neural networks are considered “black boxes” since it is difficult to explain their inner working and predictions. This lack of interpretability makes it challenging to use such models for mission-critical supply chain decisions that require explainability and auditability. Organizations need to use techniques like model inspection, SIM explanations to gain useful insights from opaque models.

Integration with Legacy Systems: Supply chain IT infrastructure in most organizations consists of legacy ERP/TMS systems that have been in use for decades. Integrating new AI/ML capabilities with these existing systems in a seamless manner requires careful planning and deployment strategies. Issues range from data/API compatibility to ensuring continuous and reliable model execution within legacy processes and workflows. Organizations need to invest in modernization efforts and plan integrations judiciously.

Technology Debt: Implementing any new technology comes with technical debt as prototypes are built, capabilities are added iteratively and systems evolve over time. With AI/ML with its fast pace of innovation, technology debt issues like outdated models, code, and infrastructure become important to manage proactively. Without due diligence, debt can lead to deteriorating performance, bugs and security vulnerabilities down the line. Organizations need to adopt best practices like continuous integration/delivery to manage this evolving technology landscape.

Talent Shortage: AI and supply chain talent with cross-functional skills are in short supply industry-wide. Building high-performing AI/ML teams requires capabilities across data science, engineering, domain expertise and more. While certain roles can be outsourced, core team members with deep technical skills and business acumen are critical for long term success but difficult to hire. Organizations need strategic talent partnerships and training programs to develop internal staff.

Regulatory Compliance: Supply chains operate in complex regulatory environments which adds extra challenges for AI. Issues range from data privacy & security to model governance, explainability for audits and non-discrimination in outputs. Frameworks like GDPR guidelines on ML require thorough due diligence. Adoption also needs to consider domain-specific regulations for industries like pharma, manufacturing etc. Regulatory knowledge gaps can delay projects or even result in non-compliance penalties.

Change Management: Implementing emerging technologies with potential for business model change and job displacements requires proactive change management. Issues range from guiding user adoption, reskilling workforce to addressing potential job displacement responsibly. Change fatigue from repeated large-scale digital transformations also needs consideration. Strong change leadership, communication and talent strategies are important for successful transformation while mitigating operational/social disruptions.

Cost of Experimentation: Building complex AI/ML supply chain applications often requires extensive experimentation with different model architectures, features, algorithms, etc. to get optimal solutions. This exploratory work has significant associated costs in terms of infrastructure spend, data processing resources, talent effort etc. Budgeting adequately for an experimental phase and establishing governance around cost controls is important. Return on investment also needs to consider tangible vs intangible benefits to justify spends.

While AI/ML offers immense opportunities to transform supply chains, their successful implementation requires diligent planning and long term commitment to address challenges across data, technology, talent, change management and regulatory compliance dimensions. Adopting best practices, piloting judiciously, establishing governance processes and fostering cross-functional collaboration are critical success factors for organizations. Continuous learning based on experiments and outcomes also helps maximize value from these emerging technologies over time.

HOW CAN HEALTHCARE ORGANIZATIONS ENSURE THAT AI ALGORITHMS ARE TRANSPARENT AND UNBIASED?

Leave a reply

Healthcare organizations have an ethical obligation to ensure AI algorithms used for clinical decision making are transparent, interpretable, and free from biases that could negatively impact patients. There are several proactive steps organizations should take.

First, organizations must commit to algorithmic transparency as a core value and establish formal governance structures, such as oversight committees, to regularly audit algorithms for biases, errors, and other issues that could compromise care. Clinicians, data scientists, ethicists, and patients should be represented on these committees to bring diverse perspectives. Their role is evaluating algorithms throughout the entire development life cycle from design to deployment.

Next, algorithm design must prioritize interpretability and explainability from the outset. “Black box” algorithms that operate as closed systems are unacceptable in healthcare. Developers should opt for intrinsically interpretable models like decision trees over complex neural networks when possible. For complex models, techniques like model exploration tools, localized surrogate models, and example-based explanations must be incorporated to provide clinicians insights into how and why algorithms generated specific predictions or recommendations for individual patients.

During model training, healthcare organizations should ensure their data and modeling protocols avoid incorporating biases. For representative clinical algorithms, training data must be thoroughly evaluated for biases related to variables like age, gender, ethnicity, socioeconomic status and more that could disadvantage already at-risk patient groups. If biases are found, data balancing or preprocessing techniques may need to be applied, or alternative data sources sought to broaden representation. Modeling choices like selection of features and outcomes must also avoid encoding human biases.

Rigorous auditing for performance differences across demographic groups is essential before and after deployment. Regular statistical testing of model predictions for different patient subpopulations can flag performance disparities requiring algorithm adjustments or alternative usage depending on severity. For example, if an algorithm consistently under- or over- predicts risk for a given group, it may need retraining with additional data from that group or restricting use cases to avoid clinical harms.

Once deployed, healthcare AI must have mechanisms for feedback and refinement. Clinicians and patients impacted by algorithm recommendations should have channels to report concerns, issues or question specific outputs. These reports warrant investigation and may trigger algorithm retraining if warranted. Organizations must also establish processes for re-evaluating algorithms as new data and medical insights emerge over time to ensure continued performance and accommodation of new knowledge.

Accessible mechanisms for consent and transparency with patients are also required. When algorithms meaningfully impact care, patients have a right to easily understand the role of AI in their treatment and opportunities to opt-out of its use without penalty. Organizations should develop digital tools and documentation empowering patients to understand the limitations and specific uses of algorithms involved in their care in non-technical language.

Ensuring unbiased, transparent healthcare AI requires sustained multidisciplinary collaboration and a culture of accountability that prioritizes patients over profits or convenience. While complex, it is an achievable standard if organizations embed these strategies and values into their algorithm design, governance, and decision-making from the ground up. With diligence, AI has tremendous potential to augment clinicians and better serve all communities, but only if its development follows guidelines protecting against harms from biased or opaque algorithms that could undermine trust in medicine.

Through formal algorithmic governance, prioritizing interpretability and oversight from concept to clinical use, carefully addressing biases in data and models, continuous performance monitoring, feedback mechanisms, and consent practices that empower patients – healthcare organizations can establish the safeguards necessary to ensure AI algorithms are transparent, intelligible and developed/applied in an unbiased manner. Upholding these standards across the medical AI field will be paramount to justify society’s trust in technology increasingly playing a role in clinical decision making.

HOW CAN GOVERNMENTS AND ORGANIZATIONS SUPPORT WORKERS IN TRANSITIONING TO NEW ROLES AS A RESULT OF TECHNOLOGICAL DISRUPTION

Leave a reply

Technological disruption through automation and artificial intelligence is likely to significantly impact many jobs and industries in the coming years. While this disruption may increase productivity and economic growth, it also risks displacing many workers who need to transition to new roles. Both governments and organizations have an important role to play in supporting workers through this transition.

To help workers transition effectively, governments should significantly increase funding for retraining and skills development programs. Workers needing to transition out of declining industries will require support to learn new skills and qualify for in-demand jobs of the future. By making community college free or low-cost, and offering grants/loans for vocational training programs, more workers can access education and retool their careers. Retraining programs should be designed based on detailed forecasts of which jobs are most likely to be impacted and which emerging jobs will need to be filled. This ensures retraining funds are targeted to support transitions into stable, growing career paths.

Governments can also establish online reemployment centers to help workers explore career options. Through skills assessments and job matching tools, these centers can guide workers towards suitable training programs based on their existing experience and skills. Centers could also offer remote digital skills courses to help workers gain qualifications for more technology-focused jobs even if they are unable to physically attend classes. Case managers at the centers can provide ongoing career coaching and help with job applications.

Meanwhile, direct financial assistance for displaced workers during their retraining period is also important. Extended unemployment benefits that last beyond traditional periods can help cover living expenses while workers upgrade their skills through longer term training programs. Targeted wage subsidies for employers who hire retrained workers getting a foothold in a new industry can further boost transitions.

Organizations undergoing technological changes also have a role to play in reskilling incumbent employees. They should provide transparency around how roles may evolve or become redundant over time so workers are aware of coming changes. Internal retraining programs focused on in-demand digital skills can help existing employees transition into newly created roles driven by technology adoption, keeping valuable institutional knowledge within the organization. Where full internal transitions are not possible, organizations should offer generous severance packages and outplacement services connecting departing employees to available training opportunities and jobs.

Governments could incentivize such organizational support through tax credits for businesses that engage in on-the-job training or fund external courses for a significant percentage of their workforce annually. Collaboration with community colleges on curriculum development ensures training aligns with emerging industry needs. This type of public-private partnership optimizes resources to support widespread, effective upskilling of displaced workers.

As automation continues, lifelong learning will become increasingly important for workers to stay employable. Governments and organizations must work together to establish an adaptive, supportive environment where workers feel empowered and equipped to continually upgrade their skills throughout their careers in response to changing job requirements. With coordinated, collaborative efforts focused on robust retraining options and financial assistance, societies can help workers successfully navigate technological disruption and transition to new opportunities.

By significantly increasing funding for well-designed retraining programs, establishing online career centers, offering direct financial assistance to displaced workers and incentivizing organizations to support upskilling, governments and organizations can play a key role in easing the disruption of technological change on workers and smoothing their transitions to emerging jobs and industries. A dedication to reskilling and lifelong learning will be vital to ensuring workers are empowered participants in our increasingly technology-driven economies.

HOW CAN THREAT INTELLIGENCE HELP ORGANIZATIONS IN THEIR INCIDENT RESPONSE EFFORTS?

Leave a reply

Threat intelligence plays a crucial role in assisting organizations with their incident response activities. When an organization experiences a security incident like a data breach, ransomware attack, or another cybersecurity event, having timely and relevant threat intelligence can help incident responders investigate what happened more quickly and effectively contain any damage.

Threat intelligence platforms collect, analyze, and distribute intelligence on cyber threats from a variety of open and closed sources. This intelligence comes in the form of indicators of compromise like malicious IP addresses and domains, malware signatures, toolkits, and techniques used by active threat actors. All of this contextual threat data provides incident responders with valuable insights into the infrastructure and behaviors of known threat groups.

During the initial assessment phase of an incident, responders can leverage threat intelligence to help characterize the nature and scope of the problem. If threat actors or malware families involved in prior attacks are mentioned in intelligence reports, responders gain an immediate understanding of the motivations and capabilities of the potential perpetrators. This context allows responders to narrow the focus of their investigation based on known tactics, techniques and procedures utilized by those groups.

Threat intelligence becomes especially important when responders need to hunt for any additional IOCs or compromised assets that were not initially observed. Integrating intelligence data with endpoint detection and network monitoring tools gives responders the ability to scan enterprise environments for the known malware signatures, IP addresses or domain names associated with the ongoing incident. This proactive hunting using confirmed IOCs shortens the amount of time it takes responders to fully contain an incident by helping them uncover any propagation that evaded initial detection.

Beyond investigating the specifics of the incident at hand, threat intelligence exposes responders to emerging risks and trends which can inform longer term mitigation efforts. Seeing how similar incidents have occurred for other organizations in intelligence reports helps responders anticipate the kinds of follow-on activities or data exfiltration attempts they may need to watch out for in the future. They gain insights into the full attack lifecycle and learn new IOCs that could become relevant for detection in coming weeks or months as groups continue to develop their infrastructure.

With a cache of current and relevant threat intelligence, response playbooks can be tailored to the known behaviors of involved actors. For example, if an attack bears the hallmarks of an advanced persistent threat group with a history of targeting sensitive information, responders may opt to conduct a more thorough data recovery and analysis in case any exfiltration occurred prior to detection. Alternately, if the threat appears financially motivated such as a ransomware deployment, responders can focus resources on asset recovery and system restoration over a detailed examination of user activities.

Threat intelligence sharing between organizations also improves incident response capabilities across sectors. When threat data is distributed in an automated, timely manner, other firms can integrate uncovered IOCs into their protections before similar attacks spread. This collective visibility shortens the overall life cycle of incidents by helping defenders stay ahead of emerging tactics. It facilitates a virtuous cycle where each organization’s experiences strengthen defenses industry-wide.

Threat intelligence serves as an invaluable backdrop for incident response teams as they work to identify compromise, mitigate damage and learn from experiences. With actionable intelligence connecting observed activity to known adversaries and campaigns, responders can investigate more methodically, proactively hunt for persistent footholds and make better prioritized decisions around containment and recovery. Regular intelligence consumption and sharing ultimately enhances an organization’s ability to respond and bolsters resilience across interconnected environments.