InfoSec and Compliance – With 20 years of blogging experience, DISC InfoSec blog is dedicated to providing trusted insights and practical solutions for professionals and organizations navigating the evolving cybersecurity landscape. From cutting-edge threats to compliance strategies, this blog is your reliable resource for staying informed and secure. Dive into the content, connect with the community, and elevate your InfoSec expertise!
API security presents several challenges for AppSec teams, including limited visibility of API endpoints, difficulty in automating and scaling tests, and maintaining consistent processes and compliance. As API estates grow with AI, keeping track of exposed endpoints becomes harder, emphasizing the need for automation tools.
Additionally, knowledge gaps in teams and limitations in current testing tools hinder effective API security. Addressing these gaps with automated testing, enhanced tools, and training can significantly improve outcomes.
Resource and time constraints make it challenging to thoroughly test APIs. Automating tests helps reduce this burden and free up resources for deeper security measures.
API security challenges are broken down into six core areas. These include the complexity of gaining visibility into API endpoints, the difficulty in automating and scaling security tests, and ensuring consistency in processes and compliance. Other concerns involve knowledge gaps among security teams and the inadequacy of current tools for effective API testing. Finally, limited resources and time constraints make comprehensive API security testing difficult, underscoring the importance of automation to alleviate these challenges and enhance protection.
The article discusses security challenges associated with large language models (LLMs) and APIs, focusing on issues like prompt injection, data leakage, and model theft. It highlights vulnerabilities identified by OWASP, including insecure output handling and denial-of-service attacks. API flaws can expose sensitive data or allow unauthorized access. To mitigate these risks, it recommends implementing robust access controls, API rate limits, and runtime monitoring, while noting the need for better protections against AI-based attacks.
The post discusses defense strategies against attacks targeting large language models (LLMs). Providers are red-teaming systems to identify vulnerabilities, but this alone isn’t enough. It emphasizes the importance of monitoring API activity to prevent data exposure and defend against business logic abuse. Model theft (LLMjacking) is highlighted as a growing concern, where attackers exploit cloud-hosted LLMs for profit. Organizations must act swiftly to secure LLMs and avoid relying solely on third-party tools for protection.
The post highlights the rapid evolution of AI bots and their growing impact on internet security. Initially, bots performed simple, repetitive tasks, but modern AI bots leverage machine learning and natural language processing to engage in more complex activities.
Types of Bots:
Good Bots: Help with tasks like web indexing and customer support.
Malicious Bots: Involved in harmful activities like data scraping, account takeovers, DDoS attacks, and fraud.
Security Impacts:
AI bots are increasingly sophisticated, making cyberattacks more complex and difficult to detect. This has led to significant data breaches, resource drains, and a loss of trust in online services.
Defense Strategies:
Organizations are employing advanced detection algorithms, multi-factor authentication (MFA), CAPTCHA systems, and collaborating with cybersecurity firms to combat these threats.
Case studies show that companies across sectors are successfully reducing bot-related incidents by implementing these measures.
Future Directions:
AI-powered security solutions and regulatory efforts will play key roles in mitigating the threats posed by evolving AI bots. Industry collaboration will also be essential to staying ahead of these malicious actors.
The rise of AI bots brings both benefits and challenges to the internet landscape. While they can provide useful services, malicious bots present serious security threats. For organizations to safeguard their assets and uphold user trust, it’s essential to understand the impact of AI bots on internet security and deploy advanced mitigation strategies. As AI technology progresses, staying informed and proactive will be critical in navigating the increasingly complex internet security environment.
The blog post discusses how ISO 27001 can help address AI-related security risks. AI’s rapid development raises data security concerns. Bridget Kenyon, a CISO and key figure in ISO 27001:2022, highlights the human aspects of security vulnerabilities and the importance of user education and behavioral economics in addressing AI risks. The article suggests ISO 27001 offers a framework to mitigate these challenges effectively.
The impact of AI on security | How ISO 27001 can help address such risks and concerns.
The blog post provides a detailed guide on conducting an ISO 27001 audit, which is crucial for ensuring compliance with information security standards. It covers both internal and certification audits, explaining their purposes, the audit process, and steps such as setting the audit criteria, reviewing documentation, conducting a field review, and reporting findings. The article also emphasizes the importance of having an independent auditor and following up on corrective actions to ensure proper risk management.
Linux admin tools help administrators manage and optimize Linux systems efficiently. They handle system monitoring, configuration, security management, and task automation. These tools streamline administrative tasks, improve performance, and enhance system security. The list also features monitoring utilities like Htop, Monit, and network tools like Iftop, ensuring administrators maintain stable, high-performing Linux environments.
Popular tools include:
Here Are The Top Linux Admin Tools
Webmin – Web-based interface for system administration, managing users, services, and configurations.
Puppet – Configuration management tool automating server provisioning, configuration, and management.
Zabbix – Open-source monitoring tool for networks, servers, and applications with alerting and reporting features.
Nagios – A network monitoring tool that provides alerts on system, network, and infrastructure issues.
Ansible – IT automation tool for configuration management, application deployment, and task automation using YAML.
Lsof – A command-line utility that lists open files and the processes used to use them.
Htop – Interactive process viewer for Unix systems, offering a visual and user-friendly alternative to the top command.
Redmine – Web-based project management and issue tracking tool, supporting multiple projects and teams.
Nmap – A network scanning tool for discovering hosts and services on a network that provides security auditing.
Monit – Utility for managing and monitoring Unix systems, capable of automatic maintenance and repair.
Nmon – Performance monitoring tool providing insights into CPU, memory, disk, and network usage.
Paessler PRTG – Comprehensive network monitoring tool with a web-based interface supporting SNMP, WMI, and other protocols.
GNOME System Monitor – Graphical application for monitoring system processes, resources, and file systems.
The SentinelOne post on cloud risk management covers key strategies to address risks in cloud environments. It outlines identifying and assessing risks, implementing security controls, and adopting best practices such as continuous monitoring and automation. The article emphasizes understanding the shared responsibility model between cloud providers and users and recommends prioritizing incident response planning. It also discusses compliance requirements, vendor risk management, and the importance of security frameworks like ISO 27k, NIST to ensure robust cloud security.
Cloud Risk Management Essentials
Neglecting it can lead to data breaches, fines, and reputational damage.
Understand the shared responsibility model between your obligations and your cloud providers.
Encrypt data, use strong access controls, and regularly patch vulnerabilities.
Keep up with the latest security trends and best practices.
Ensure sensitive data is handled securely throughout its lifecycle.
The article highlights how ransomware groups like BianLian and Rhysida are exploiting Microsoft Azure Storage Explorer for data exfiltration. Originally designed for managing Azure storage, this tool is now being repurposed by hackers to transfer stolen data to cloud storage. Attackers use Azure’s capabilities, such as AzCopy, to move large amounts of sensitive information. Security teams are advised to monitor logs for unusual activity, particularly around file transfers and Azure Blob storage connections, to detect and prevent such breaches.
To understand the implications of using Azure Storage Explorer for data exfiltration, it is essential to grasp the basics of Azure Blob Storage. It consists of three key resources:
Storage Account: The overarching entity that provides a namespace for your data.
Container: A logical grouping within the storage account that holds your blobs.
Blob: The actual data object stored within a container.
This structure is similar to storage systems used by other public cloud providers, like Amazon S3 and Google Cloud Storage.
AzCopy Logging and Analysis – The Key to Detecting Data Theft
Azure Storage Explorer uses AzCopy, a command-line tool, to handle data transfers. It generates detailed logs during these transfers, offering a crucial avenue for incident responders to identify data exfiltration attempts.
By default, Azure Storage Explorer and AzCopy use the “INFO” logging level, which captures key events such as file uploads, downloads, and copies. The log entries can include:
UPLOADSUCCESSFUL and UPLOADFAILED: Indicate the outcome of file upload operations.
DOWNLOADSUCCESSFUL and DOWNLOADFAILED: Reveal details of files brought into the network from Azure.
COPYSUCCESSFUL and COPYFAILED: Show copying activities across different storage accounts.
The logs are stored in the .azcopy directory within the user’s profile, offering a valuable resource for forensic analysis.
Logging Settings and Investigation Challenges
Azure Storage Explorer provides a “Logout on Exit” setting, which is disabled by default. This default setting retains any valid Azure Storage sessions when the application is reopened, potentially allowing threat actors to continue their activities even after initial investigations.
At the end of the AzCopy log file, investigators can find a summary of job activities, providing an overview of the entire data transfer operation. This final summary can be instrumental in understanding the scope of data exfiltration carried out by the attackers.
Indicators of Compromise (IOCs)
Detecting the use of Azure Storage Explorer by threat actors involves recognizing certain Indicators of Compromise (IOCs) on the system. The following paths and files may suggest the presence of data exfiltration activities:
File Paths:
%USERPROFILE%\AppData\Local\Programs\Microsoft Azure Storage Explorer
The post discusses whether ISO 27001 certification is worth it, highlighting its benefits like improved reputation, enhanced security, and competitive advantage. ISO 27001 offers a comprehensive framework for managing information security risks, focusing on people, processes, and technology. Certification, though not mandatory, provides independent validation of an organization’s commitment to security, which can also reduce penalties in case of data breaches. It positions organizations to stand out, especially in regulated industries like finance and healthcare.
The article emphasizes the growing importance of cybersecurity as a boardroom priority in today’s digital economy. With cyber risks increasing, cybersecurity is no longer just a technical issue; it is a critical concern that board members must address to safeguard business operations, reputations, and financial health.
Key points include:
Cyber Threats Are Escalating: The frequency and severity of attacks like phishing and ransomware are rising, with the average cost of a data breach hitting $4.88 million. This creates both immediate and long-term impacts, such as financial loss, regulatory fines, and reputational damage.
Board Engagement Is Crucial: Board members must actively engage in shaping cybersecurity strategies, understanding key threats, allocating resources, and fostering a security culture throughout the organization.
Proactive Measures for Resilience: Boards should implement comprehensive cybersecurity frameworks (ISO, NIST e.g.,) prioritize employee training, and ensure robust incident response plans. Regular security assessments and simulations can help mitigate risks.
In summary, cybersecurity must be integrated into business strategy, with board members leading the charge to protect the organization’s future and maintain stakeholder trust. Cybersecurity is now a strategic imperative, essential for long-term resilience and sustainable growth.
The article explains how to enhance the security of Infrastructure as Code (IaC) by default. It emphasizes integrating security policies into CI/CD pipelines, automating IaC scanning, and using the application as the source of truth for infrastructure needs. It highlights the risks of manual code handling, such as human error and outdated templates, and discusses the challenges of automated remediation. The solution lies in abstracting IaC using tools that generate infrastructure based on application needs, ensuring secure, compliant infrastructure.
Making Infrastructure as Code (IaC) secure is crucial for maintaining the security of cloud environments and preventing vulnerabilities from being introduced during deployment. Here are some best practices to ensure the security of IaC:
1. Use Secure IaC Tools
Trusted Providers: Use reputable IaC tools like Terraform, AWS CloudFormation, or Ansible that have strong security features.
Keep Tools Updated: Ensure that your IaC tools and associated libraries are always updated to the latest version to avoid known vulnerabilities.
2. Secure Code Repositories
Access Control: Limit access to IaC repositories to authorized personnel only, using principles of least privilege.
Use Git Best Practices: Use branch protection rules, mandatory code reviews, and signed commits to ensure that changes to IaC are audited and authorized.
Secrets Management: Never hardcode sensitive information (like API keys or passwords) in your IaC files. Use secret management solutions like AWS Secrets Manager, HashiCorp Vault, or environment variables.
3. Enforce Security in Code
Static Code Analysis (SAST): Use tools like Checkov, TFLint, or Terraform Sentinel to analyze your IaC for misconfigurations, like open security groups or publicly accessible S3 buckets.
Linting and Formatting: Enforce code quality using linters (e.g., tflint for Terraform) that check for potential security misconfigurations early in the development process.
4. Follow Least Privilege for Cloud Resources
Role-based Access Control (RBAC): Configure your cloud resources with the minimum permissions needed. Avoid overly permissive IAM roles or policies, such as using wildcard * permissions.
Security Groups: Ensure that security groups and firewall rules are configured to limit network access to only what is required.
5. Monitor and Audit IaC Changes
Version Control: Use version control systems like Git to track changes to your IaC. This helps maintain audit trails and facilitates rollbacks if needed.
Automated Testing: Implement continuous integration (CI) pipelines to automatically test and validate IaC changes before deployment. Include security tests in your pipeline.
6. Secure IaC Execution Environment
Control Deployment Access: Limit access to the environment where the IaC code will be executed (e.g., Jenkins, CI/CD pipelines) to authorized personnel.
Use Signed IaC Templates: Ensure that your IaC templates or modules are signed to verify their integrity.
7. Encrypt Data
Data at Rest and In Transit: Ensure that all sensitive data, such as configuration files, is encrypted using cloud-native encryption solutions (e.g., AWS KMS, Azure Key Vault).
Use SSL/TLS: Use SSL/TLS certificates to secure communication between services and prevent man-in-the-middle (MITM) attacks.
8. Regularly Scan for Vulnerabilities
Security Scanning: Regularly scan your IaC code for known vulnerabilities and misconfigurations using security scanning tools like Trivy or Snyk IaC.
Penetration Testing: Conduct regular penetration testing to identify weaknesses in your IaC configuration that might be exploited by attackers.
9. Leverage Policy as Code
Automate Compliance: Use policy-as-code frameworks like Open Policy Agent (OPA) to define and enforce security policies across your IaC deployments automatically.
10. Train and Educate Teams
Security Awareness: Ensure that your teams are trained in secure coding practices and are aware of cloud security principles.
IaC-Specific Training: Provide training specific to the security risks of IaC, including common misconfigurations and how to avoid them.
By integrating security into your IaC practices from the beginning, you can prevent security vulnerabilities from being introduced during the deployment process and ensure that your cloud infrastructure remains secure.
Currently, the cyber security approach for MSP clients includes steps like End User Security Awareness, Patching, EDR, Access Control, Vulnerability Management, and SIEM implementation—essentially throwing various tools at the problem.
However, what if we’ve had it backwards? Shouldn’t we start by asking why each control is necessary and if it matches the client’s risk profile? Clients are seeking change and are tired of outdated methods.
Instead of merely adding services, we should start with vision, foresight, and leadership, embodying the principles of a vCISO. It’s about building a foundation of strategic brilliance, not just following the continuum but redefining it. Rethink Cybersecurity—Start with Vision, Start with vCISO.
MSP, or Managed Service Provider, plays a crucial role in safeguarding businesses from cyber threats by managing information asset risks and delivering Information Security Management services, acting as a vCISO at both tactical and strategic levels.
Helping maintain compliance: MSPs can help organizations maintain compliance to various standards and regulations.
MSPs can help reduce the burden on internal IT/InfoSec teams.
Enhancing cyber resilience: MSPs can help enhance overall maturity of InfoSec program.
The article lists 33 open-source cybersecurity tools designed to improve security for various platforms, including Linux, Windows, and macOS. These tools cover a wide range of security needs, from identity management and encryption to vulnerability scanning, threat intelligence, and forensic analysis. Examples include Authentik for identity management, Grype for vulnerability scanning, and MISP for threat intelligence sharing. These solutions offer flexibility and transparency, enabling organizations to customize their security infrastructure.
Open-source cybersecurity tools provide transparency and flexibility, allowing users to examine and customize the source code to fit specific security needs. These tools make cybersecurity accessible to a broader range of organizations and individuals.
In this article, you will find a list of 33 open-source cybersecurity tools for Linux, Windows, and macOS that you should consider to enhance protection and stay ahead of potential threats.
The article emphasizes that AI cybersecurity must be multi-layered, like the systems it protects. Cybercriminals increasingly exploit large language models (LLMs) with attacks such as data poisoning, jailbreaks, and model extraction. To counter these threats, organizations must implement security strategies during the design, development, deployment, and operational phases of AI systems. Effective measures include data sanitization, cryptographic checks, adversarial input detection, and continuous testing. A holistic approach is needed to protect against growing AI-related cyber risks.
Benefits and Concerns of AI in Data Security and Privacy
Predictive analytics provides substantial benefits in cybersecurity by helping organizations forecast and mitigate threats before they arise. Using statistical analysis, machine learning, and behavioral insights, it highlights potential risks and vulnerabilities. Despite hurdles such as data quality, model complexity, and the dynamic nature of threats, adopting best practices and tools enhances its efficacy in threat detection and response. As cyber risks evolve, predictive analytics will be essential for proactive risk management and the protection of organizational data assets.
AI raises concerns about data privacy and security. Ensuring that AI tools comply with privacy regulations and protect sensitive information.
AI systems must adhere to privacy laws and regulations, such as GDPR, CPRA to protect individuals’ information. Compliance ensures ethical data handling practices.
Implementing robust security measures to protect data (data governance) from unauthorized access and breaches is critical. Data protection practices safeguard sensitive information and maintain trust.
1. Predictive Analytics in Cybersecurity
Predictive analytics offers substantial benefits by helping organizations anticipate and prevent cyber threats before they occur. It leverages statistical models, machine learning, and behavioral analysis to identify potential risks. These insights enable proactive measures, such as threat mitigation and vulnerability management, ensuring an organization’s defenses are always one step ahead.
2. AI and Data Privacy
AI systems raise concerns regarding data privacy and security, especially as they process sensitive information. Ensuring compliance with privacy regulations like GDPR and CPRA is crucial. Organizations must prioritize safeguarding personal data while using AI tools to maintain trust and avoid legal ramifications.
3. Security and Data Governance
Robust security measures are essential to protect data from breaches and unauthorized access. Implementing effective data governance ensures that sensitive information is managed, stored, and processed securely, thus maintaining organizational integrity and preventing potential data-related crises.
Recent research shows that Predator spyware, once believed to be inactive due to U.S. sanctions, has resurfaced with improved evasion tactics. Despite efforts to curtail its usage, Predator is still being used in countries like the Democratic Republic of the Congo (DRC) and Angola, where it targets high-profile individuals. Its updated infrastructure makes it more difficult to track victims, underscoring the need for strong cybersecurity defenses. Risk mitigation strategies include regular software updates, enabling lockdown modes, and deploying mobile device management systems. As spyware becomes more sophisticated, international collaboration is crucial to regulating and limiting its spread.
Predator spyware, once linked to Intellexa, has resurfaced after a period of reduced activity, despite sanctions and exposure. The reactivated spyware infrastructure poses renewed threats to privacy and security, as operators have adopted new techniques to obscure their activities, making it harder to track and attribute attacks. With capabilities like remote device infiltration and data exfiltration, governments can secretly monitor citizens and gather sensitive information. Predator’s operators have strengthened their infrastructure by adding another layer of anonymization to their multi-tiered delivery system, making it more difficult to trace the origin and usage of the spyware. Though the attack methods, including “one-click” and “zero-click” exploits, remain similar, the increased complexity of the infrastructure heightens the threat to high-profile individuals such as politicians, executives, journalists, and activists. The expensive licensing of Predator indicates its use is reserved for strategic targets, raising concerns in the European Union, where investigations have uncovered its misuse against opposition figures and journalists in countries like Greece and Poland. To counter the threat of Predator spyware, individuals and organizations should prioritize security measures like regular software updates, device reboots, and lockdown modes. Mobile device management (MDM) systems and security awareness training are also essential in protecting against social engineering and advanced spyware attacks. As the demand for surveillance tools grows, the spyware market continues to expand, with new companies developing increasingly sophisticated tools. While there are ongoing discussions around stricter regulations, particularly following investigations by Insikt Group, the threat of spyware will persist until meaningful international action is taken.
For more detailed insights, check the full article here.
In an era where digital connectivity has become ubiquitous, the line between privacy and surveillance has blurred. Nowhere is this more evident than in the proliferation of spy apps – discreet, powerful tools that grant unprecedented access to the lives of unsuspecting individuals. From tracking location and monitoring communications to covertly capturing audio and video, these applications represent a double-edged sword in the realm of technology.
The rise of artificial intelligence (AI) has introduced new risks in software supply chains, particularly through open-source repositories like Hugging Face and GitHub. Cybercriminals, such as the NullBulge group, have begun targeting these repositories to poison data sets used for AI model training. These poisoned data sets can introduce misinformation or malicious code into AI systems, causing widespread disruption in AI-driven software and forcing companies to retrain models from scratch.
With AI systems relying heavily on vast open-source data sets, attackers have found it easier to infiltrate AI development pipelines. Compromised data sets can result in severe disruptions across AI supply chains, especially for businesses refining open-source models with proprietary data. As AI adoption grows, the challenge of maintaining data integrity, compliance, and security in open-source components becomes crucial for safeguarding AI advancements.
Open-source data sets are vital to AI development, as only large enterprises can afford to train models from scratch. However, these data sets, like LAION 5B, pose risks due to their size, making it difficult to ensure data quality and compliance. Cybercriminals exploit this by poisoning data sets, introducing malicious information that can compromise AI models. This ripple effect forces costly retraining efforts. The popularity of generative AI has further attracted attackers, heightening the risks across the entire AI supply chain.
The article emphasizes the importance of integrating security into all stages of AI development and usage, given the rise of AI-targeted cybercrime. Businesses must ensure traceability and explainability for AI outputs, keeping humans involved in the process. AI shouldn’t be seen solely as a cost-cutting tool, but rather as a technology that needs robust security measures. AI-powered security solutions can help analysts manage threats more effectively but should complement, not replace, human expertise.
For more detailed insights, check the full article here.
The article discusses the increasing financial impact of cybercrime on businesses, with attacks like ransomware and DDoS causing significant losses. Average costs for DDoS attacks have risen to $6,000 per minute, while ransomware payouts have skyrocketed, with a record-breaking $75 million ransom paid in 2024. Third-party vendor breaches and industry-specific vulnerabilities are also contributing to escalating costs.
Companies are facing growing pressure to address these threats, yet many are struggling with cybersecurity talent shortages and burnout. Despite paying ransoms, recovery costs continue to rise, and cyber insurance often doesn’t cover all expenses. Investing in preventive measures and continuous monitoring is critical to mitigate risks.
For more detailed insights, check the full article here.
The article from IBM emphasizes the critical role of data governance in ensuring high-quality, secure, and accessible data, which is vital for organizations aiming to leverage emerging technologies like AI, ML, and automation.
Effective data governance acts like air traffic control, managing the flow of data to ensure integrity and prevent misuse. Without proper governance, organizations risk basing decisions on inaccurate data or suffering breaches that can lead to financial losses and erode trust. Data governance also ensures organizations have access to real-time, high-quality data, enabling them to make better business decisions, optimize operations, and maintain compliance with regulations.
Establishing an effective data governance framework requires a long-term commitment, collaboration across departments, and thoughtful implementation. Organizations should start small, define roles and responsibilities, secure stakeholder buy-in, and select the right tools to manage data. Continuous monitoring, improvement, and alignment with broader business strategies are essential for sustained success. Strong data security practices, adherence to privacy regulations, and the use of maturity models help organizations build a dynamic governance ecosystem that evolves alongside the business, fostering a culture that views data as a strategic asset.
The IBM blog on AI risk management discusses how organizations can identify, mitigate, and address potential risks associated with AI technologies. AI risk management is a subset of AI governance, focusing specifically on preventing and addressing threats to AI systems. The blog outlines various types of risks—such as data, model, operational, and ethical/legal risks—and emphasizes the importance of frameworks like the NIST AI Risk Management Framework to ensure ethical, secure, and reliable AI deployment. Effective AI risk management enhances security, decision-making, regulatory compliance, and trust in AI systems.
AI risk management can help close this gap and empower organizations to harness AI systems’ full potential without compromising AI ethics or security.
Understanding the risks associated with AI systems
Like other types of security risk, AI risk can be understood as a measure of how likely a potential AI-related threat is to affect an organization and how much damage that threat would do.
While each AI model and use case is different, the risks of AI generally fall into four buckets:
Data risks
Model risks
Operational risks
Ethical and legal risks
The NIST AI Risk Management Framework (AI RMF)
In January 2023, the National Institute of Standards and Technology (NIST) published the AI Risk Management Framework (AI RMF) to provide a structured approach to managing AI risks. The NIST AI RMF has since become a benchmark for AI risk management.
The AI RMF’s primary goal is to help organizations design, develop, deploy and use AI systems in a way that effectively manages risks and promotes trustworthy, responsible AI practices.
Developed in collaboration with the public and private sectors, the AI RMF is entirely voluntary and applicable across any company, industry or geography.
The framework is divided into two parts. Part 1 offers an overview of the risks and characteristics of trustworthy AI systems. Part 2, the AI RMF Core, outlines four functions to help organizations address AI system risks:
Govern: Creating an organizational culture of AI risk management
Map: Framing AI risks in specific business contexts
Predictive analytics offers significant benefits in cybersecurity by allowing organizations to foresee and mitigate potential threats before they occur. Using methods such as statistical analysis, machine learning, and behavioral analysis, predictive analytics can identify future risks and vulnerabilities. While challenges like data quality, model complexity, and evolving threats exist, employing best practices and suitable tools can improve its effectiveness in detecting cyber threats and managing risks. As cyber threats evolve, predictive analytics will be vital in proactively managing risks and protecting organizational information assets.
Trust Me: ISO 42001 AI Management System is the first book about the most important global AI management system standard: ISO 42001. The ISO 42001 standard is groundbreaking. It will have more impact than ISO 9001 as autonomous AI decision making becomes more prevalent.
Why Is AI Important?
AI autonomous decision making is all around us. It is in places we take for granted such as Siri or Alexa. AI is transforming how we live and work. It becomes critical we understand and trust this prevalent technology:
“Artificial intelligence systems have become increasingly prevalent in everyday life and enterprise settings, and they’re now often being used to support human decision making. These systems have grown increasingly complex and efficient, and AI holds the promise of uncovering valuable insights across a wide range of applications. But broad adoption of AI systems will require humans to trust their output.” (Trustworthy AI, IBM website, 2024)
