RiskSpotlight的动态

With Bill 194 out in Ontario, the?Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024, public sector entities are required to adopt robust measures for the responsible use of AI. This includes developing accountability frameworks, managing risks, and ensuring transparency in AI operations. At C3SA Cyber Security & Audit, we are excited to be partnering with AGAT Software AI, whose?BusinessGPT., a security and governance firewall for AI, is well aligned to meet these needs. Here's how: Key AI Requirements of Bill 194: Transparency: Public sector entities must provide information about their use of AI systems. Accountability: Development and implementation of accountability frameworks for AI use. Risk Management: Steps to manage risks associated with AI systems. Oversight: Ensuring oversight and compliance with prescribed regulations The BusinessGPT AI Firewall?offers comprehensive visibility and control over GenAI usage, ensuring full oversight of how it is utilized and what data is involved, providing transparency and accountability. It enforces risk-based policies tailored for AI services, mitigating risks associated with GenAI use while ensuring compliance with regulatory standards. For organizations that prefer to avoid exposing their data to public AI services like ChatGPT and Microsoft Copilot, BusinessGPT provides a?Private AI Solution. This secure and customizable chatbot includes advanced Retrieval-Augmented Generation (RAG) and data analysis capabilities. It supports self-hosting and on-premises deployment options, safeguarding data privacy while meeting stringent accountability requirements. Organizations (public or private) can harness the benefits of GenAI, stay competitive, and manage risks effectively without compromising on data security or compliance. Curious? ??Please reach out. #AI #CyberSecurity #PublicSector #Ontario #BusinessGPT #AGATSoftware #DigitalTransformation

?? Artificial Intelligence (AI) has swiftly evolved, reshaping industries and driving innovation. However, this rapid advancement has outpaced current regulatory frameworks, particularly regarding data security and privacy. Tim Freestone emphasizes that while frameworks like the NIST AI Risk Management Framework and Executive Order 14110 have made strides, they fall short in critical areas such as access controls and data tracking. This oversight exposes AI systems to significant risks, including data breaches, privacy violations, and loss of public trust. Freestone proposes a robust approach to fill these gaps by prioritizing data security through zero trust principles. Implementing least-privilege access, continuous monitoring, and stringent tracking mechanisms can significantly enhance data protection. This approach ensures regulatory compliance, reduces algorithmic bias, and fosters public trust, paving the way for responsible AI innovation. Key Points: ?? Current AI regulations lack comprehensive data security measures. ?? Zero trust principles can enhance AI data protection. ?? Proper data handling can mitigate risks and build public trust. #AI #DataSecurity #Privacy #ZeroTrust #AIRegulation #TechInnovation #Cybersecurity #DataProtection

The first AI Certification is here: ISO 42001 ISO/IEC 42001:2023 establishes a comprehensive framework for the management of artificial intelligence (AI) systems within organizations. It emphasises the importance of ethical, secure, and transparent AI development and deployment. This section outlines the core components and technical specifications of ISO 42001, providing guidance on AI management, risk and impact assessments, and addressing data protection and AI security. Core Components of the ISO 42001 Standard The ISO 42001 standard is structured around several core components that are essential for the effective management of AI systems: AI Management Systems (AIMS): Integration with organisational processes to ensure continuous improvement and alignment with other ISO standards. AI Risk Assessment: A systematic approach to identifying and mitigating risks throughout the AI lifecycle. AI Impact Assessment: Evaluation of the consequences of AI on individuals and societies. Data Protection and AI Security: Emphasis on compliance with privacy laws and safeguarding AI systems against threats. Technical Specifications Guiding AI Management The technical specifications of ISO 42001 provide detailed guidance on: Establishing and maintaining an AI management system that is coherent with organisational goals and ethical standards. Implementing procedures for continuous monitoring and improvement of AI systems. Ensuring that AI systems are designed and deployed in a manner that respects privacy, security, and ethical considerations. Requirements for AI Risk and Impact Assessments Under ISO 42001, organizations are required to: Conduct comprehensive AI risk assessments to identify potential risks to users and society. Perform AI impact assessments to understand the broader consequences of AI deployment on individuals and communities. Develop and implement strategies to mitigate identified risks and minimise negative impacts. Addressing Data Protection and AI Security ISO 42001 places a strong emphasis on: Ensuring AI systems comply with applicable data protection laws and regulations. Implementing robust security measures to protect AI systems from unauthorized access, data breaches, and other cyber threats. Maintaining transparency in AI decision-making processes to foster trust and accountability. By adhering to the guidelines and requirements set forth in ISO 42001, organizations can navigate the complexities of AI management, ensuring that their AI systems are not only effective but also ethical, secure, and aligned with global standards. #ISO42001 #AI #Cybersecurity #business

Best practices for securing and ensuring the resilience of #AIsystems include: -?Apply a risk-based approach to AI adoption with a wide range of stakeholders involved in managing the risks end-to-end within the organization. -?Create an inventory of AI applications to assess how and where AI is being used within the organization, including whether it is part of the mission-critical #supplychain. -?Ensure that there is adequate investment in the essential #cybersecurity controls needed to protect AI systems and ensure that they are prepared to respond to and recover from disruptions. Key practices include robust #threat and vulnerability management practices, implementing controls for protecting the perimeters of systems – such as segmentation of networks and databases and data-loss prevention, beside segregation of duties and ensuring that the AI systems and the infrastructure hosting AI algorithms and #data are protected by access controls such as #MFA and #PAM. -?Implement technical controls around the AI systems with people- and process-based controls on the interface between the technology and business operations. -?Give extra care to information governance specifically, what data will be exposed to the AI and what controls are needed to ensure that organizational data policies are met. Also, secure sharing sensitive information with AI system

??? ???????????????????????? ???????????????? ???????????????????????????? ???? ?????? ?????? ???? ????: ????????’?? ?????????????????????????? ???????????????????? ?? Artificial intelligence (AI) is transforming critical infrastructure by enhancing efficiency and predictive capabilities. Yet, this progress introduces significant risks, including AI-driven cyberattacks and systemic vulnerabilities. Recognizing these challenges, the Cybersecurity and Infrastructure Security Agency (CISA) has released comprehensive guidelines rooted in the NIST AI Risk Management Framework. These measures aim to help sectors embrace AI innovation while safeguarding public safety and critical services from disruption. ?? CISA’s guidelines emphasize a lifecycle-based framework: Govern, Map, Measure, and Manage. Key priorities include securing AI systems against adversarial manipulation, preventing design failures, and enhancing resilience through human oversight and validation. The framework also highlights data integrity, mitigation of AI-enabled social engineering, and addressing emerging risks like generative AI misuse. By fostering partnerships among AI vendors, industry leaders, and regulators, the guidelines create a united front against evolving threats. ?? As a cybersecurity professional, I find these guidelines transformative and essential. The reliance on AI in critical infrastructure necessitates a culture of "security-first innovation." Challenges like inscrutability, bias, and overreliance on AI demand a human-centric approach that combines technical safeguards with workforce training, transparent AI practices, and active threat modeling. Measures like adversarial testing, vendor accountability, and rigorous validation are non-negotiable to prevent catastrophic failures. By embedding these principles, we can unlock AI’s potential while ensuring its safe integration into vital systems. ?? How do you see AI transforming risk management in critical infrastructure? What steps are you taking to align AI innovation with robust cybersecurity practices? https://lnkd.in/gfaYknhQ #artificialintelligence #criticalinfrastructure #cybersecurityinai #cybersecurity #cyberriskmanagement

Protecting Business Context: The Cornerstone of AI Security In the age of AI, your business context is your competitive advantage—and safeguarding it is no longer optional. From sensitive customer insights to proprietary strategies, context shapes the decisions AI makes. Without proper protection, businesses risk losing more than data; they risk losing their edge. Why Business Context Needs Ironclad Security: ?? Data Breaches Are Just the Beginning: A leaked context can expose strategic plans, competitive intelligence, or confidential customer information—crippling your business. ?? AI Models Learn What They See: Compromised context could result in flawed AI predictions, decisions, and actions, affecting everything from operations to customer trust. ?? Regulatory Compliance Risks: With tighter regulations like GDPR and CCPA, mishandling sensitive business data can lead to legal and financial repercussions. Steps to Secure Your Business Context: ? Data Encryption: Protect sensitive data both in transit and at rest to prevent unauthorized access. ? Role-Based Access: Ensure only authorized team members have access to critical AI inputs and outputs. ? Regular Audits: Continuously monitor and evaluate your AI systems for vulnerabilities and compliance. ? Context Isolation: Segregate sensitive business context to minimize exposure during collaborations or AI training. ? AI Explainability: Use models that provide transparency, allowing you to trace decisions back to the source context. Your AI is only as secure as the business context it relies on. By treating your context like the strategic asset it is, you protect not just your data, but your business’s future. Is your AI security strategy up to the challenge? #AI #BusinessSecurity #ContextMatters #Cybersecurity #DataProtection #AIInnovation

#snsinstitution #snsdesignthinking #designthinkers Article about Gamma Al 1.Data Classification: Gamma AI's technology can automatically identify and classify sensitive information within an organization's digital environment. For example, it can recognize confidential documents, personal information, or financial records. By classifying this data, companies can better control who has access to it. 2.Data Loss Prevention (DLP): One of the main features of Gamma AI is its ability to prevent data loss. The system monitors outgoing communications (like emails) and stops sensitive information from being sent to unauthorized parties. This is crucial for maintaining privacy and complying with regulations. 3.Compliance: Many industries have strict rules about how data should be handled. Gamma AI helps companies comply with these rules by ensuring that sensitive data is stored, used, and shared according to legal requirements. This is particularly important for industries like finance, healthcare, and legal services. 4.Real-Time Monitoring: Gamma AI provides real-time monitoring of data, allowing companies to respond quickly to any potential security threats. If the system detects suspicious activity, it can alert administrators or automatically take steps to protect the data. 5.AI and Machine Learning: The company uses advanced AI and machine learning techniques to continually improve its detection and protection capabilities. The more data the system processes, the better it becomes at identifying and securing sensitive information. Why Is Gamma AI Important? In today’s digital world, data is one of the most valuable assets a company can have. However, with increasing cyber threats, protecting that data has become more challenging. Gamma AI addresses this challenge by providing tools that help companies safeguard their data more effectively. By using AI, Gamma AI can process large amounts of data quickly and accurately, offering a more efficient solution than traditional methods. This not only enhances security but also helps companies avoid the high costs associated with data breaches, such as fines, legal fees, and damage to their reputation. Conclusion Gamma AI is at the forefront of using artificial intelligence to protect sensitive data. By offering advanced data classification, loss prevention, and compliance solutions, it helps companies secure their valuable information and stay compliant with regulations.

?? Introducing Guardium AI Security: Protecting AI, Data, and Business ?? ?? As AI adoption accelerates, organizations face critical challenges: - Hidden AI Risks: Shadow AI deployments are operating without visibility, leaving security teams unaware of their connections to sensitive data and applications. - Weak Security Posture: Vulnerabilities like account takeovers, misconfigurations, and excessive permissions expose AI systems to breaches and attacks such as prompt injection. - Complex Compliance Requirements: Evolving regulations demand strict oversight of AI deployments and data usage, with hefty fines for non-compliance. ???? AI innovation is a game-changer—but without the right safeguards, it can lead to: - Data Breaches: The interaction between models, data, and apps can create unforeseen vulnerabilities. - Operational Disruption: Compromised AI systems can jeopardize critical processes. - Regulatory Penalties: Non-compliance with global data privacy and AI governance frameworks can result in financial and reputational damage. ??IBM created Guardium AI Security to empower organizations to confidently secure their AI deployments: - Unparalleled Visibility: Discover and inventory all AI models, including shadow AI, across multi-cloud and multi-vendor environments. - Proactive Risk Management: Automated risk scoring prioritizes vulnerabilities, with actionable recommendations to mitigate threats. - OWASP Top 10 Alignment: Follow industry-leading frameworks to protect your AI systems from emerging attack vectors. - Integrated Compliance: Seamlessly align with global regulations using tools like watsonx.governance for a holistic view of security and business risk. With Guardium AI Security, organizations can innovate with confidence, secure sensitive data, and stay ahead of evolving threats and regulations. Ready to safeguard your AI-driven future? Let’s connect! #AI #CyberSecurity #GuardiumAI #DataProtection #Innovation #Compliance

Prioritizing Data Security: A Commitment to Integrity in AI Development At Nomisma, data security isn't just a checkbox—it's the foundation of everything we do. As a company dedicated to building custom AI solutions, we understand the immense value that data holds in the modern world, and the responsibility that comes with it. Why It Matters: In an era where information drives decisions, we prioritize safeguarding your data at every step. From AI model training to deployment, we ensure that every layer of our solutions is designed with security as the top priority. What We Do: End-to-End Encryption: Data is encrypted both in transit and at rest, ensuring that it’s secure no matter where it is in the pipeline. Compliance First: Our solutions are built to meet the highest global data protection standards, including GDPR, HIPAA, and more. Transparency & Control: You remain in control of your data at all times. We provide you with the tools to monitor, audit, and manage access to your sensitive information. ?? Vision: We believe in a future where integrity of data is at the core of all AI solutions. A world where businesses can innovate without sacrificing security, and where trust is built through robust data practices. At Nomisma, we are working towards that future every day. Let's build AI solutions that are not only intelligent but also ethical and secure. #DataSecurity #AI #BespokeSolutions #Innovation #DataPrivacy #EthicalAI #TrustInTechnology #Cybersecurity