Jun 03 2025

IBM’s model-routing approach

Category: AIdisc7 @ 4:14 pm

IBM’s model-routing approach—where a model-routing algorithm acts as an orchestrator—is part of a growing trend in AI infrastructure known as multi-model inference orchestration. Let’s break down what this approach involves and why it matters:


🔄 What It Is

Instead of using a single large model (like a general-purpose LLM) for all inference tasks, IBM’s approach involves multiple specialized models—each potentially optimized for different domains, tasks, or modalities (e.g., text, code, image, or legal reasoning).

At the center of this architecture sits a routing algorithm, which functions like a traffic controller. When an inference request (e.g., a user prompt) comes in, the router analyzes it and predicts which model is best suited to handle it based on context, past performance, metadata, or learned patterns.


⚙️ How It Works (Simplified Flow)

  1. Request Input: A user sends a prompt (e.g., a question or task).
  2. Router Evaluation: The orchestrator examines the request’s content—this might involve analyzing intent, complexity, or topic (e.g., legal vs. creative writing).
  3. Model Selection: Based on predefined rules, statistical learning, or even another ML model, the router selects the optimal model from a pool.
  4. Forwarding & Inference: The request is forwarded to the chosen model, which generates the response.
  5. Feedback Loop (optional): Performance outcomes can be fed back to improve future routing decisions.


🧠 Why It’s Powerful

  • Efficiency: Lighter or more task-specific models can be used instead of always relying on a massive general model—saving compute costs.
  • Performance: Task-optimized models may outperform general LLMs in niche domains (e.g., finance, medicine, or law).
  • Scalability: Multiple models can be run in parallel and updated independently.
  • Modularity: Easier to plug in or retire models without affecting the whole system.


📊 Example Use Case

Suppose a user asks:

  • “Summarize this legal contract.”
    The router detects legal language and routes to a model fine-tuned on legal documents.

If instead the user asks:

  • “Write a poem about space,”
    It could route to a creative-writing-optimized model.

AI Value Creators: Beyond the Generative AI User Mindset

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Tags: IBM model-routing


Jun 03 2025

10 Practical Tips to Spot and Stop Phishing Emails Before It’s Too Late

Category: Information Security,Phishingdisc7 @ 12:16 pm

🔟 Phishing Tips:

  1. Suspicious Offers
    Be wary of emails offering free money or alarming threats (e.g., frozen accounts). These emotional triggers are classic phishing tactics.
  2. Free Money Red Flag
    Phishing often exploits greed—if something sounds too good to be true, it probably is.
  3. Generic Greetings
    Emails that don’t address you personally (e.g., “Dear customer”) are likely mass phishing attempts.
  4. Urgency Traps
    Don’t act on emails that pressure you to respond immediately—urgency is a common manipulation tactic.
  5. Requests for Personal Info
    Legitimate organizations won’t ask for sensitive information via email. Don’t provide personal or business data.
  6. Bad Grammar, Bad Sign
    Poor spelling and awkward grammar are red flags that an email may be a phishing attempt.
  7. Suspicious File Attachments
    Avoid opening uncommon file types (e.g., .exe, .js, .vbs)—they often carry malware.
  8. Mismatch in Sender Info
    Always compare the sender’s name to the actual email address to spot spoofing attempts.
  9. Check Before Clicking Links
    Hover over links to see the actual URL before clicking—phishers often disguise malicious sites.
  10. Email Header Clues
    Review email headers if you’re suspicious; a sketchy history is a clear sign to delete the email.


Feedback

This tip sheet provides clear, actionable guidance and covers the essentials of phishing detection well. The advice is practical for both technical and non-technical users, with an emphasis on behavior-based awareness. Overall, it’s a solid tool for raising awareness and promoting a culture of cautious clicking.

Phishing Prevention Guide: The psychology behind phishing scams | How hackers use phishing | Email & SMS scam prevention | Real-world phishing attack examples | Defending against phishing

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Tags: phishing


Jun 03 2025

Top 5 AI-Powered Scams to Watch Out for in 2025

Category: AI,Security Awarenessdisc7 @ 8:00 am

1. Deep-fake celebrity impersonations
Scammers now mass-produce AI-generated videos, photos, or voice clips that convincingly mimic well-known figures. The fake “celebrity” pushes a giveaway, investment tip, or app download, lending instant credibility and reach across social platforms and ads. Because the content looks and sounds authentic, victims lower their guard and click through.

2. “Too-good-to-fail” crypto investments
Fraud rings promise eye-watering returns on digital-currency schemes, often reinforced by forged celebrity endorsements or deep-fake interviews. Once funds are transferred to the scammers’ wallets, they vanish—and the cross-border nature of the crime makes recovery almost impossible.

3. Cloned apps and look-alike websites
Attackers spin up near-pixel-perfect copies of banking apps, customer-support portals, or employee login pages. Entering credentials or card details hands them straight to the crooks, who may also drop malware for future access or ransom. Even QR codes and app-store listings are spoofed to lure downloads.

4. Landing-page cloaking
To dodge automated scanners, scammers show Google’s crawlers a harmless page while serving users a malicious one—often phishing forms or scareware purchase screens. The mismatch (“cloaking”) lets the fraudulent ad or search result slip past filters until victims report it.

5. Event-driven hustles
Whenever a big election, disaster, eclipse, or sporting final hits the headlines, fake charities, ticket sellers, or NASA-branded “special glasses” pop up overnight. The timely hook plus fabricated urgency (“donate now or miss out”) drives impulsive clicks and payments before scrutiny kicks in.

6. Quick take
Google’s May-2025 advisory is a solid snapshot of how criminals are weaponizing generative AI and marketing tactics in real time. Its tips (check URLs, doubt promises, use Enhanced Protection, etc.) are sound, but the bigger lesson is behavioral: pause before you pay, download, or share credentials—especially when a message leans on urgency or authority. Technology can flag threats, yet habitual skepticism remains the best last-mile defense.

Protecting Yourself: Stay Away from AI Scams

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Tags: AI Fraud, AI scams, AI-Powered Scams


Jun 02 2025

Summary of CISO 3.0: Leading AI Governance and Security in the Boardroom

Category: AI,CISO,Information Security,vCISOdisc7 @ 5:12 pm

  1. Aaron McCray, Field CISO at CDW, discusses the evolving role of the Chief Information Security Officer (CISO) in the age of artificial intelligence (AI). He emphasizes that CISOs are transitioning from traditional cybersecurity roles to strategic advisors who guide enterprise-wide AI governance and risk management. This shift, termed “CISO 3.0,” involves aligning AI initiatives with business objectives and compliance requirements.
  2. McCray highlights the challenges of integrating AI-driven security tools, particularly regarding visibility, explainability, and false positives. He notes that while AI can enhance security operations, it also introduces complexities, such as the need for transparency in AI decision-making processes and the risk of overwhelming security teams with irrelevant alerts. Ensuring that AI tools integrate seamlessly with existing infrastructure is also a significant concern.
  3. The article underscores the necessity for CISOs and their teams to develop new skill sets, including proficiency in data science and machine learning. McCray points out that understanding how AI models are trained and the data they rely on is crucial for managing associated risks. Adaptive learning platforms that simulate real-world scenarios are mentioned as effective tools for closing the skills gap.
  4. When evaluating third-party AI tools, McCray advises CISOs to prioritize accountability and transparency. He warns against tools that lack clear documentation or fail to provide insights into their decision-making processes. Red flags include opaque algorithms and vendors unwilling to disclose their AI models’ inner workings.
  5. In conclusion, McCray emphasizes that as AI becomes increasingly embedded across business functions, CISOs must lead the charge in establishing robust governance frameworks. This involves not only implementing effective security measures but also fostering a culture of continuous learning and adaptability within their organizations.

Feedback

  1. The article effectively captures the transformative impact of AI on the CISO role, highlighting the shift from technical oversight to strategic leadership. This perspective aligns with the broader industry trend of integrating cybersecurity considerations into overall business strategy.
  2. By addressing the practical challenges of AI integration, such as explainability and infrastructure compatibility, the article provides valuable insights for organizations navigating the complexities of modern cybersecurity landscapes. These considerations are critical for maintaining trust in AI systems and ensuring their effective deployment.
  3. The emphasis on developing new skill sets underscores the dynamic nature of cybersecurity roles in the AI era. Encouraging continuous learning and adaptability is essential for organizations to stay ahead of evolving threats and technological advancements.
  4. The cautionary advice regarding third-party AI tools serves as a timely reminder of the importance of due diligence in vendor selection. Transparency and accountability are paramount in building secure and trustworthy AI systems.
  5. The article could further benefit from exploring specific case studies or examples of organizations successfully implementing AI governance frameworks. Such insights would provide practical guidance and illustrate the real-world application of the concepts discussed.
  6. Overall, the article offers a comprehensive overview of the evolving responsibilities of CISOs in the context of AI integration. It serves as a valuable resource for cybersecurity professionals seeking to navigate the challenges and opportunities presented by AI technologies.

For further details, access the article here

AI is rapidly transforming systems, workflows, and even adversary tactics, regardless of whether our frameworks are ready. It isn’t bound by tradition and won’t wait for governance to catch up…When AI evaluates risks, it may enhance the speed and depth of risk management but only when combined with human oversight, governance frameworks, and ethical safeguards.

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Tags: AI Governance, CISO 3.0


Jun 01 2025

AI in the Workplace: Replacing Tasks, Not People

Category: AIdisc7 @ 3:48 pm

  1. Establishing an AI Strategy and Guardrails:
    To effectively integrate AI into an organization, leadership must clearly articulate the company’s AI strategy to all employees. This includes defining acceptable and unacceptable uses of AI, legal boundaries, and potential risks. Setting clear guardrails fosters a culture of responsibility and mitigates misuse or misunderstandings.
  2. Transparency and Job Impact Communication:
    Transparency is essential, especially since many employees may worry that AI initiatives threaten their roles. Leaders should communicate that those who adapt to AI will outperform those who resist it. It’s also important to outline how AI will alter jobs by automating routine tasks, thereby allowing employees to focus on higher-value work.
  3. Redefining Roles Through AI Integration:
    For instance, HR professionals may shift from administrative tasks—like managing transfers or answering policy questions—to more strategic work such as improving onboarding processes. This demonstrates how AI can enhance job roles rather than eliminate them.
  4. Addressing Employee Sentiments and Fears:
    Leaders must pay attention to how employees feel and what they discuss informally. Creating spaces for feedback and development helps surface concerns early. Ignoring this can erode culture, while addressing it fosters trust and connection. Open conversations and vulnerability from leadership are key to dispelling fear.
  5. Using AI to Facilitate Dialogue and Action:
    AI tools can aid in gathering and classifying employee feedback, sparking relevant discussions, and supporting ongoing engagement. Digital check-ins powered by AI-generated prompts offer structured ways to begin conversations and address concerns constructively.
  6. Equitable Participation and Support Mechanisms:
    Organizations must ensure all employees are given equal opportunity to engage with AI tools and upskilling programs. While individuals will respond differently, support systems like centralized feedback platforms and manager check-ins can help everyone feel included and heard.

Feedback and Organizational Tone Setting:
This approach sets a progressive and empathetic tone for AI adoption. It balances innovation with inclusion by emphasizing transparency, emotional intelligence, and support. Leaders must model curiosity and vulnerability, signaling that learning is a shared journey. Most importantly, the strategy recognizes that successful AI integration is as much about culture and communication as it is about technology. When done well, it transforms AI from a job threat into a tool for empowerment and growth.

Resolving Routine Business Activities by Harnessing the Power of AI: A Competency-Based Approach that Integrates Learning and Information with … Workbooks for Structured Learning

p.s. “AGI shouldn’t be confused with GenAI. GenAI is a tool. AGI is a
goal of evolving that tool to the extent that its capabilities match
human cognitive abilities, or even surpasses them, across a wide
range of tasks. We’re not there yet, perhaps never will be, or per
haps it’ll arrive sooner than we expected. But when it comes to
AGI, think about LLMs demonstrating and exceeding humanlike
intelligence”

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Tags: AI Governance


May 30 2025

How Cybersecurity Experts Are Strengthening Defenses with AWS Tools

Category: AWS Security,cyber security,Security Toolsdisc7 @ 12:19 pm

The article “How cyber security professionals are leveraging AWS tools” from Computer Weekly provides an in-depth look at how organizations utilize Amazon Web Services (AWS) to enhance their cybersecurity posture. Here is a rephrased summary of the key points and tools discussed, followed by my feedback.

1. Centralized Cloud Visibility and Operations

AWS offers cybersecurity professionals a unified view of their cloud environments, facilitating smoother operations. Tools like AWS CloudTrail and AWS Config enable teams to manage access, detect anomalies, and ensure real-time policy compliance. Integration with platforms such as Recorded Future further enhances risk orchestration capabilities.

2. Foundational Tools for Multi-Cloud Environments

In multi- or hybrid-cloud setups, AWS CloudTrail and AWS GuardDuty serve as foundational tools. They provide comprehensive insights into cloud activities, aiding in the identification and resolution of issues affecting corporate systems.

3. Scalability for Threat Analysis

AWS’s scalability is invaluable for threat analysis. It allows for the efficient processing of large volumes of threat data and supports the deployment of isolated research environments, maintaining the integrity of research infrastructures.

4. Comprehensive Security Toolset

Organizations like Graylog utilize a suite of AWS tools—including GuardDuty, Security Hub, Config, CloudTrail, Web Application Firewall (WAF), Inspector, and Identity and Access Management (IAM)—to secure customer instances. These tools are instrumental in anomaly detection, compliance, and risk management.

5. AI and Machine Learning Integration

AWS’s integration of artificial intelligence (AI) and machine learning (ML) enhances threat detection capabilities. These technologies power background threat tracking and provide automated alerts for security issues, data leaks, and suspicious activities, enabling proactive responses to potential crises.

6. Interoperability and Scalable Security Architecture

The interoperability of AWS tools like GuardDuty, Config, and IAM Access Analyzer allows for the creation of a scalable and cohesive security architecture. This integration is crucial for real-time monitoring, security posture management, and prevention of privilege sprawl.

7. Enhanced Threat Intelligence

AWS’s advanced threat intelligence capabilities, supported by AI-driven tools, enable the detection of sophisticated cyber threats. The platform’s ability to process vast amounts of data aids in identifying and responding to emerging threats effectively.

8. Support for Compliance and Risk Management

AWS tools assist organizations in meeting compliance requirements and managing risks. By providing detailed logs and monitoring capabilities, these tools support adherence to regulatory standards and internal security policies.

Feedback

The article effectively highlights the multifaceted ways in which AWS tools bolster cybersecurity efforts. The integration of AI and ML, coupled with a comprehensive suite of security tools, positions AWS as a robust platform for managing modern cyber threats. However, organizations must remain vigilant and ensure they are leveraging these tools to their full potential, continuously updating their strategies to adapt to the evolving threat landscape.

For further details, access the article here

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Tags: AWS tools, cybersecurity


May 29 2025

Why CISOs Must Prioritize Data Provenance in AI Governance

Category: AI,IT Governancedisc7 @ 9:29 am

In the rapidly evolving landscape of artificial intelligence (AI), Chief Information Security Officers (CISOs) are grappling with the challenges of governance and data provenance. As AI tools become increasingly integrated into various business functions, often without centralized oversight, the traditional methods of data governance are proving inadequate. The core concern lies in the assumption that popular or “enterprise-ready” AI models are inherently secure and compliant, leading to a dangerous oversight of data provenance—the ability to trace the origin, transformation, and handling of data.

Data provenance is crucial in AI governance, especially with large language models (LLMs) that process and generate data in ways that are often opaque. Unlike traditional systems where data lineage can be reconstructed, LLMs can introduce complexities where prompts aren’t logged, outputs are copied across systems, and models may retain information without clear consent. This lack of transparency poses significant risks in regulated domains like legal, finance, or privacy, where accountability and traceability are paramount.

The decentralized adoption of AI tools across enterprises exacerbates these challenges. Various departments may independently implement AI solutions, leading to a sprawl of tools powered by different LLMs, each with its own data handling policies and compliance considerations. This fragmentation means that security organizations often lose visibility and control over how sensitive information is processed, increasing the risk of data breaches and compliance violations.

Contrary to the belief that regulations are lagging behind AI advancements, many existing data protection laws like GDPR, CPRA, and others already encompass principles applicable to AI usage. The issue lies in the systems’ inability to respond to these regulations effectively. LLMs blur the lines between data processors and controllers, making it challenging to determine liability and ownership of AI-generated outputs. In audit scenarios, organizations must be able to demonstrate the actions and decisions made by AI tools, a capability many currently lack.

To address these challenges, modern AI governance must prioritize infrastructure over policy. This includes implementing continuous, automated data mapping to track data flows across various interfaces and systems. Records of Processing Activities (RoPA) should be updated to include model logic, AI tool behavior, and jurisdictional exposure. Additionally, organizations need to establish clear guidelines for AI usage, ensuring that data handling practices are transparent, compliant, and secure.

Moreover, fostering a culture of accountability and awareness around AI usage is essential. This involves training employees on the implications of using AI tools, encouraging responsible behavior, and establishing protocols for monitoring and auditing AI interactions. By doing so, organizations can mitigate risks associated with AI adoption and ensure that data governance keeps pace with technological advancements.

CISOs play a pivotal role in steering their organizations toward robust AI governance. They must advocate for infrastructure that supports data provenance, collaborate with various departments to ensure cohesive AI strategies, and stay informed about evolving regulations. By taking a proactive approach, CISOs can help their organizations harness the benefits of AI while safeguarding against potential pitfalls.

In conclusion, as AI continues to permeate various aspects of business operations, the importance of data provenance in AI governance cannot be overstated. Organizations must move beyond assumptions of safety and implement comprehensive strategies that prioritize transparency, accountability, and compliance. By doing so, they can navigate the complexities of AI adoption and build a foundation of trust and security in the digital age.

For further details, access the article here on Data provenance

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Tags: data provenance


May 28 2025

What is Amazon Bedrock and how can Amazon bedrock assist in GRC field

Category: AWS Security,GRCdisc7 @ 3:40 pm

Amazon Bedrock is a fully managed service offered by Amazon Web Services (AWS) that provides foundation models (FMs) from leading AI companies through a single API. It allows developers to build and scale generative AI applications without the need to manage the underlying infrastructure or train their own large language models.

In the context of Governance, Risk, and Compliance (GRC), Amazon Bedrock can assist in several ways:

  1. Policy Analysis and Creation:
    • Analyze existing policies and regulations with different standards and regulations
      • Generate drafts of new policies or updates to existing ones
      • Summarize complex regulatory documents
    • Risk Assessment:
      • Analyze data to identify potential risks
      • Generate risk reports and summaries
      • Assist in creating risk mitigation strategies
    • Compliance Monitoring:
      • Analyze large volumes of data to identify compliance issues
      • Generate compliance reports
      • Assist in creating action plans for addressing compliance gaps
    • Automated Auditing:
      • Analyze audit logs and generate reports
      • Identify patterns or anomalies that may indicate compliance issues
      • Assist in creating audit trails and documentation
    • Training and Education:
      • Generate training materials on GRC topics
      • Create quizzes or assessments to test employee knowledge
      • Provide personalized learning experiences based on individual needs
    • Document Management:
      • Classify and organize GRC-related documents
      • Extract key information from documents
      • Generate summaries of lengthy reports or regulations
    • Incident Response:
      • Analyze incident reports to identify trends or patterns
      • Generate incident response plans
      • Assist in root cause analysis
    • Regulatory Intelligence:
      • Monitor and analyze regulatory changes
      • Summarize new regulations and their potential impact
      • Assist in creating action plans to address new regulatory requirements
    • Stakeholder Communication:
      • Generate drafts of reports for different stakeholders
      • Assist in creating presentations on GRC topics
      • Summarize complex GRC issues for non-technical audiences
    • Predictive Analytics:
      • Analyze historical data to predict future risks or compliance issues
      • Assist in scenario planning and what-if analysis

    To leverage Amazon Bedrock for these GRC applications, organizations would need to:

    1. Choose appropriate foundation models available through Bedrock
    2. Fine-tune these models with domain-specific data if necessary
    3. Develop applications that integrate with Bedrock’s API
    4. Implement proper security and access controls
    5. Ensure compliance with data privacy regulations when using the service

    By utilizing Amazon Bedrock, GRC professionals can potentially increase efficiency, improve accuracy, and gain deeper insights into their governance, risk, and compliance processes. However, it’s important to note that while AI can assist in these areas, human oversight and expertise remain crucial in the GRC field.

    DISC can help you create an agent in Bedrock and integrate it with your S3 bucket.

    Analyzing data to identify potential risks is a crucial part of risk management. Here’s a step-by-step approach to this process:

    1. Data Collection:
      • Gather relevant data from various sources (financial reports, operational metrics, incident reports, external market data, etc.)
      • Ensure data quality and completeness
    2. Data Preparation:
      • Clean the data to remove errors or inconsistencies
      • Normalize data to ensure consistency across different sources
      • Structure the data for analysis (e.g., creating a unified database or data warehouse)
    3. Define Risk Categories:
      • Identify the types of risks you’re looking for (e.g., financial, operational, strategic, compliance)
      • Establish key risk indicators (KRIs) for each category
    4. Statistical Analysis:
      • Perform descriptive statistics to understand data distributions
      • Look for outliers or anomalies that might indicate potential risks
      • Use correlation analysis to identify relationships between variables
    5. Trend Analysis:
      • Analyze historical data to identify trends over time
      • Look for patterns that might indicate emerging risks
    6. Predictive Modeling:
      • Use techniques like regression analysis or machine learning to predict future risks
      • Develop models that can forecast potential risk scenarios
    7. Scenario Analysis:
      • Conduct “what-if” analyses to understand potential impacts of different risk scenarios
      • Use stress testing to assess how well the organization can withstand extreme events
    8. Data Visualization:
      • Create visual representations of the data (charts, graphs, heat maps)
      • Use dashboards to provide an overview of key risk indicators
    9. Text Analysis:
      • If dealing with unstructured data (like customer complaints or social media), use natural language processing techniques to extract insights
    10. Risk Mapping:
      • Map identified risks to business processes or objectives
      • Assess the potential impact and likelihood of each risk
    11. Comparative Analysis:
      • Compare your risk profile with industry benchmarks or historical data
      • Identify areas where your risk exposure differs significantly from peers or past performance
    12. Interdependency Analysis:
      • Identify connections between different risks
      • Assess how risks might compound or trigger each other
    13. Continuous Monitoring:
      • Set up systems for real-time or near-real-time risk monitoring
      • Establish alerts for when key risk indicators exceed predefined thresholds
    14. Expert Review:
      • Have subject matter experts review the analysis results
      • Incorporate qualitative insights to complement the data-driven analysis
    15. Feedback Loop:
      • Regularly review and refine your analysis methods
      • Update your risk identification process based on new data and learnings

    To implement this process effectively, you might use a combination of tools:

    • Statistical software (like R or Python with libraries such as pandas, scikit-learn)
    • Business intelligence tools (like Tableau or Power BI for visualization)
    • Specialized risk management software
    • Machine learning platforms for more advanced predictive analytics

    Remember, while data analysis is powerful for identifying potential risks, it should be combined with human expertise and judgment. Some risks may not be easily quantifiable or may require contextual understanding that goes beyond what the data alone can provide.

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    Tags: Amazon Bedrock, Amazon Bedrock Agents, AWS


    May 24 2025

    A comprehensive competitive intelligence analysis tailored to an Information Security Compliance and vCISO services business:

    Category: Information Security,Security Compliance,vCISOdisc7 @ 11:20 am

    1. Industry Landscape Overview

    Market Trends

    • Increased Regulatory Complexity: With GDPR, CCPA, HIPAA, and emerging regulations like DORA (EU), EU AI Act businesses are seeking specialized compliance partners.
    • SME Cybersecurity Prioritization: Mid-sized businesses are investing in vCISO services to bridge expertise gaps without hiring full-time CISOs.
    • Rise of Cyber Insurance: Insurers are demanding evidence of strong compliance postures, increasing demand for third-party audits and vCISO engagements.

    Growth Projections

    • vCISO market is expected to grow at 17–20% CAGR through 2028.
    • Compliance automation tools, Process orchestration (AI) and advisory services are growing due to demand for cost-effective solutions.

    2. Competitor Landscape

    Direct Competitors

    • Virtual CISO Services by Cynomi, Fractional CISO, and SideChannel
      • Offer standardized packages, onboarding frameworks, and clear SLA-based services.
      • Differentiate through cost, specialization (e.g., healthcare, fintech), and automation integration.

    Indirect Competitors

    • MSSPs and GRC Platforms like Arctic Wolf, Drata, Vanta
      • Provide automated compliance dashboards, sometimes bundled with consulting.
      • Threat: Position as “compliance-as-a-service,” reducing perceived need for vCISO.

    3. Differentiation Levers

    What Works in the Market

    • Vertical Specialization: Deep focus on industries like legal, SaaS, fintech, or healthcare adds credibility.
    • Thought Leadership: Regular LinkedIn posts, webinars, and compliance guides elevate visibility and trust.
    • Compliance-as-a-Path-to-Growth: Reframing compliance as a revenue enabler (e.g., “SOC 2 = more enterprise clients”) resonates well.

    Emerging Niches

    • vDPO (Virtual Data Protection Officer) in the EU market.
    • Posture Maturity Consulting for startups seeking Series A or B funding.
    • Third-Party Risk Management-as-a-Service as vendor scrutiny rises.

    4. SWOT Analysis

    StrengthsWeaknesses
    Deep expertise in InfoSec & complianceMay lack scalability without automation
    Custom vCISO engagementsHigh-touch model limits price elasticity
    OpportunitiesThreats
    Demand surge in SMBs & startupsCommoditization by automated GRC tools
    Cross-border compliance needs (e.g., UK GDPR + US laws)Emerging AI-based compliance tools (OneTrust AI, etc.)

    5. Positioning Strategy

    Target Segments

    • Series A–C Startups: Need compliance to grow and satisfy investors.
    • Regulated SMEs: Especially fintech, healthtech, legal tech.
    • Private Equity & M&A: Require due diligence, risk posture reviews.

    Key Messaging Pillars

    • “Board-ready reporting without the CISO salary.”
    • “Compliance as a strategic differentiator, not just a checkbox.”
    • “Scale securely—fractional leadership for fast-growth companies.”

    6. Strategic Recommendations

    Product Strategy

    • Offer tiered vCISO packages (e.g., Startup, Growth, Enterprise).
    • Add compliance automation tool integrations (e.g., Vanta, Drata).
    • Develop TPRM offering with a vendor risk scorecard framework.

    Go-To-Market Strategy

    • Use LinkedIn and niche SaaS podcasts for lead gen.
    • Co-market with GRC tool vendors (bundle advisory with tech).
    • Run quarterly compliance clinics/webinars—capture leads.

    Brand Strategy

    • Build credibility via certifications (ISO 27001 Lead Auditor/ Lead Implementer, CIPP/E).
    • Publish “State of Compliance Readiness” reports biannually.
    • Promote client success stories (SOC 2 audits passed, cyber insurance approved, etc.)

    DISC InfoSec vCISO Services

    ISO 27k Compliance, Audit and Certification

    AIMS and Data Governance

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    Tags: Information Security Compliance, vCISO


    May 23 2025

    Interpretation of Ethical AI Deployment under the EU AI Act

    Category: AIdisc7 @ 5:39 am

    Scenario: A healthcare startup in the EU develops an AI system to assist doctors in diagnosing skin cancer from images. The system uses machine learning to classify lesions as benign or malignant.

    1. Risk-Based Classification

    • EU AI Act Requirement: Classify the AI system into one of four risk categories: unacceptable, high-risk, limited-risk, minimal-risk.
    • Interpretation in Scenario:
      The diagnostic system qualifies as a high-risk AI because it affects people’s health decisions, thus requiring strict compliance with specific obligations.

    2. Data Governance & Quality

    • EU AI Act Requirement: High-risk AI systems must use high-quality datasets to avoid bias and ensure accuracy.
    • Interpretation in Scenario:
      The startup must ensure that training data are representative of all demographic groups (skin tones, age ranges, etc.) to reduce bias and avoid misdiagnosis.

    3. Transparency & Human Oversight

    • EU AI Act Requirement: Users should be aware they are interacting with an AI system; meaningful human oversight is required.
    • Interpretation in Scenario:
      Doctors must be clearly informed that the diagnosis is AI-assisted and retain final decision-making authority. The system should offer explainability features (e.g., heatmaps on images to show reasoning).

    4. Robustness, Accuracy, and Cybersecurity

    • EU AI Act Requirement: High-risk AI systems must be technically robust and secure.
    • Interpretation in Scenario:
      The AI tool must maintain high accuracy under diverse conditions and protect patient data from breaches. It should include fallback mechanisms if anomalies are detected.

    5. Accountability and Documentation

    • EU AI Act Requirement: Maintain detailed technical documentation and logs to demonstrate compliance.
    • Interpretation in Scenario:
      The startup must document model architecture, training methodology, test results, and monitoring processes, and be ready to submit these to regulators if required.

    6. Registration and CE Marking

    • EU AI Act Requirement: High-risk systems must be registered in an EU database and undergo conformity assessments.
    • Interpretation in Scenario:
      The startup must submit their system to a notified body, demonstrate compliance, and obtain CE marking before deployment.

    AI Governance: Applying AI Policy and Ethics through Principles and Assessments

    ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

    ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

    Businesses leveraging AI should prepare now for a future of increasing regulation.

    Digital Ethics in the Age of AI 

    DISC InfoSec’s earlier posts on the AI topic

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    Tags: Digital Ethics, EU AI Act, ISO 42001


    May 22 2025

    AI Data Security Report

    Category: AI,data securitydisc7 @ 1:41 pm

    Summary of the AI Data Security Report

    The AI Data Security report, jointly authored by the NSA, CISA, FBI, and cybersecurity agencies from Australia, New Zealand, and the UK, provides comprehensive guidance on securing data throughout the AI system lifecycle. It emphasizes the critical importance of data integrity and confidentiality in ensuring the reliability of AI outcomes. The report outlines best practices such as implementing data encryption, digital signatures, provenance tracking, secure storage solutions, and establishing a robust trust infrastructure. These measures aim to protect sensitive, proprietary, or mission-critical data used in AI systems.

    Key Risk Areas and Mitigation Strategies

    The report identifies three primary data security risks in AI systems:

    1. Data Supply Chain Vulnerabilities: Risks associated with sourcing data from external providers, which may introduce compromised or malicious datasets.
    2. Poisoned Data: The intentional insertion of malicious data into training datasets to manipulate AI behavior.
    3. Data Drift: The gradual evolution of data over time, which can degrade AI model performance if not properly managed.

    To mitigate these risks, the report recommends rigorous validation of data sources, continuous monitoring for anomalies, and regular updates to AI models to accommodate changes in data patterns.

    Feedback and Observations

    The report offers a timely and thorough framework for organizations to enhance the security of their AI systems. By addressing the entire data lifecycle, it underscores the necessity of integrating security measures from the initial stages of AI development through deployment and maintenance. However, the implementation of these best practices may pose challenges, particularly for organizations with limited resources or expertise in AI and cybersecurity. Therefore, additional support in the form of training, standardized tools, and collaborative initiatives could be beneficial in facilitating widespread adoption of these security measures.

    For further details, access the report: AI Data Security Report

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    Tags: AI Data Security


    May 22 2025

    AI in the Legislature: Promise, Pitfalls, and the Future of Lawmaking

    Category: AI,Security and privacy Lawdisc7 @ 9:00 am

    Bruce Schneier’s essay, “AI-Generated Law,” delves into the emerging role of artificial intelligence in legislative processes, highlighting both its potential benefits and inherent risks. He examines global developments, such as the United Arab Emirates’ initiative to employ AI for drafting and updating laws, aiming to accelerate legislative procedures by up to 70%. This move is part of a broader strategy to transform the UAE into an “AI-native” government by 2027, with a substantial investment exceeding $3 billion. While this approach has garnered attention, it’s not entirely unprecedented. In 2023, Porto Alegre, Brazil, enacted a local ordinance on water meter replacement, drafted with the assistance of ChatGPT—a fact that was not disclosed to the council members at the time. Such instances underscore the growing trend of integrating AI into legislative functions worldwide.

    Schneier emphasizes that the integration of AI into lawmaking doesn’t necessitate formal procedural changes. Legislators can independently utilize AI tools to draft bills, much like they rely on staffers or lobbyists. This democratization of legislative drafting tools means that AI can be employed at various governmental levels without institutional mandates. For example, since 2020, Ohio has leveraged AI to streamline its administrative code, eliminating approximately 2.2 million words of redundant regulations. Such applications demonstrate AI’s capacity to enhance efficiency in legislative processes.

    The essay also addresses the potential pitfalls of AI-generated legislation. One concern is the phenomenon of “confabulation,” where AI systems might produce plausible-sounding but incorrect or nonsensical information. However, Schneier argues that human legislators are equally prone to errors, citing the Affordable Care Act’s near downfall due to a typographical mistake. Moreover, he points out that in non-democratic regimes, laws are often arbitrary and inhumane, regardless of whether they are drafted by humans or machines. Thus, the medium of law creation—human or AI—doesn’t inherently guarantee justice or fairness.

    A significant concern highlighted is the potential for AI to exacerbate existing power imbalances. Given AI’s capabilities, there’s a risk that those in power might use it to further entrench their positions, crafting laws that serve specific interests under the guise of objectivity. This could lead to a veneer of neutrality while masking underlying biases or agendas. Schneier warns that without transparency and oversight, AI could become a tool for manipulation rather than a means to enhance democratic processes.

    Despite these challenges, Schneier acknowledges the potential benefits of AI in legislative contexts. AI can assist in drafting clearer, more consistent laws, identifying inconsistencies, and ensuring grammatical precision. It can also aid in summarizing complex bills, simulating potential outcomes of proposed legislation, and providing legislators with rapid analyses of policy impacts. These capabilities can enhance the legislative process, making it more efficient and informed.

    The essay underscores the inevitability of AI’s integration into lawmaking, driven by the increasing complexity of modern governance and the demand for efficiency. As AI tools become more accessible, their adoption in legislative contexts is likely to grow, regardless of formal endorsements or procedural changes. This organic integration poses questions about accountability, transparency, and the future role of human judgment in crafting laws.

    In reflecting on Schneier’s insights, it’s evident that while AI offers promising tools to enhance legislative efficiency and precision, it also brings forth challenges that necessitate careful consideration. Ensuring transparency in AI-assisted lawmaking processes is paramount to maintain public trust. Moreover, establishing oversight mechanisms can help mitigate risks associated with bias or misuse. As we navigate this evolving landscape, a balanced approach that leverages AI’s strengths while safeguarding democratic principles will be crucial.

    For further details, access the article here

    Artificial Intelligence: Legal Issues, Policy, and Practical Strategies

    AIMS and Data Governance

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    Tags: #Lawmaking, AI, AI Laws, AI legislature


    May 21 2025

    $167 Million Ruling Against NSO Group: What It Means for Spyware and Global Security

    Category: Spywaredisc7 @ 3:13 pm

    $167 Million Ruling Against NSO Group: What It Means for Spyware and Global Security

    1. Landmark Ruling Against NSO Group After six years of courtroom battles, a jury has delivered a powerful message: no one is above the law—not even a state-affiliated spyware vendor. NSO Group, the Israeli company behind the notorious Pegasus spyware, has been ordered to pay $167 million for illegally hacking over 1,000 individuals via WhatsApp. This penalty is the largest ever imposed in the commercial spyware sector.

    2. The Pegasus Exploit NSO’s flagship product, Pegasus, exploited a vulnerability in WhatsApp to inject malicious code into users’ phones. Approximately 1,400 devices were targeted, with victims ranging from journalists and activists to dissidents and government critics across multiple countries. This massive breach sparked international outrage and legal action.

    3. Violation of U.S. Law While a judge had previously ruled that NSO violated U.S. anti-hacking laws, this trial was focused on determining financial damages. In addition to the $167 million fine, the company was ordered to pay $440,000 in legal costs, signaling a strong stand against cyber intrusion under the guise of state security.

    4. Courtroom Accountability This case marked the first time NSO executives were compelled to testify in court. Their defense—that selling only to governments shielded them from liability—was rejected. The court’s decision emphasized that state affiliation doesn’t grant immunity when human rights are at stake.

    5. Inside NSO’s Operations Court documents revealed the scale of NSO’s operations: 140 engineers working to breach mobile devices and apps. Pegasus can extract messages, emails, images, and more—even those protected by encryption. Some attacks require no user interaction and leave virtually no trace.

    6. Broader Implications for Global Security Though NSO claims its spyware isn’t deployed within the U.S., other similar tools aren’t bound by such restrictions. This underscores the urgent need for secure communication practices, especially within government institutions. Even encrypted apps like Signal are vulnerable if a device itself is compromised.

    7. Opinion: The Future of Spyware and How to Contain It This ruling sets a precedent, but the fight against spyware is far from over. As demand persists, especially among authoritarian regimes, containment will require:

    • Binding international regulations on surveillance tech.
    • Increased transparency from both public and private sectors.
    • Sanctions on malicious spyware actors.
    • Wider adoption of secure, open-source platforms.

    Spyware like Pegasus represents a direct threat to privacy and democratic freedoms. The NSO case proves that legal accountability is possible—and necessary. The global community must now act to ensure this isn’t a one-off, but the beginning of a new era in digital rights protection.

    How a Spy in Our Pocket Threatens the End of Privacy

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    Tags: NSO Group, Pegasus


    May 21 2025

    8 domains of CISSP

    Category: CISSP,Information Securitydisc7 @ 1:24 pm

    The Certified Information Systems Security Professional (CISSP) certification encompasses eight domains that collectively form the (ISC)² Common Body of Knowledge (CBK). These domains provide a comprehensive framework for information security professionals. Below is a summarized overview of each domain:


    What are the 8 CISSP domains?

    CISSP domainCurrent weighting
    (effective 1 May 2021)
    Revised weighting
    (effective 15 April 2024)
    1. Security and Risk Management15%16%
    2. Asset Security10%10%
    3. Security Architecture and Engineering13%13%
    4. Communication and Network Security13%13%
    5. Identity and Access Management (IAM)13%13%
    6. Security Assessment and Testing12%12%
    7. Security Operations13%13%
    8. Software Development Security11%10%

    We respectfully disagree with reducing the emphasis on Domain 8. In our view, it deserves equal importance alongside Domain 1.

    CISSP exam preparation course covers these eight domains in depth.


    1. Security and Risk Management

    This domain establishes the foundational principles of information security, including confidentiality, integrity, and availability. It covers governance, compliance, risk management, and professional ethics, ensuring that security strategies align with organizational goals and legal requirements.


    2. Asset Security

    Focusing on the protection of organizational assets, this domain addresses the classification, ownership, and handling of information and resources. It ensures that data is appropriately labeled, stored, and protected according to its sensitivity and value.


    3. Security Architecture and Engineering

    This domain delves into the design and implementation of secure systems. It encompasses security models, engineering processes, and the integration of security controls into hardware, software, and network architectures to mitigate vulnerabilities.


    4. Communication and Network Security

    Covering the secure design and management of network infrastructures, this domain includes topics such as secure communication channels, network protocols, and the protection of data in transit. It ensures the confidentiality and integrity of information exchanged across networks.


    5. Identity and Access Management (IAM)

    IAM focuses on the mechanisms that control user access to information systems. It includes identification, authentication, authorization, and accountability processes to ensure that only authorized individuals can access specific resources.


    6. Security Assessment and Testing

    This domain emphasizes the evaluation of security controls and processes. It involves conducting assessments, audits, and testing to identify vulnerabilities, ensure compliance, and validate the effectiveness of security measures.


    7. Security Operations

    Focusing on the day-to-day tasks necessary to maintain and monitor security, this domain includes incident response, disaster recovery, and the management of operational security controls. It ensures the continuous protection of information systems.


    8. Software Development Security

    This domain addresses the integration of security practices into the software development lifecycle. It covers secure coding principles, threat modeling, and the identification and mitigation of vulnerabilities in software applications.


    Each domain plays a critical role in building a comprehensive understanding of information security, preparing professionals to effectively protect and manage organizational assets.

    CISSP exam preparation course covers these eight domains in depth.

    Tags: CISSP exam


    May 20 2025

    Balancing Innovation and Risk: Navigating the Enterprise Impact of AI Agent Adoption

    Category: AIdisc7 @ 3:29 pm

    The rapid integration of AI agents into enterprise operations is reshaping business landscapes, offering both significant opportunities and introducing new challenges. These autonomous systems are enhancing productivity by automating complex tasks, leading to increased efficiency and innovation across various sectors. However, their deployment necessitates a reevaluation of traditional risk management approaches to address emerging vulnerabilities.

    A notable surge in enterprise AI adoption has been observed, with reports indicating a 3,000% increase in AI/ML tool usage. This growth underscores the transformative potential of AI agents in streamlining operations and driving business value. Industries such as finance, manufacturing, and healthcare are at the forefront, leveraging AI for tasks ranging from fraud detection to customer service automation.

    Despite the benefits, the proliferation of AI agents has led to heightened cybersecurity concerns. The same technologies that enhance efficiency are also being exploited by malicious actors to scale attacks, as seen with AI-enhanced phishing and data leakage incidents. This duality emphasizes the need for robust security measures and continuous monitoring to safeguard enterprise systems.

    The integration of AI agents also brings forth challenges related to data governance and compliance. Ensuring that AI systems adhere to regulatory standards and ethical guidelines is paramount. Organizations must establish clear policies and frameworks to manage data privacy, transparency, and accountability in AI-driven processes.

    Furthermore, the rapid development and deployment of AI agents can outpace an organization’s ability to implement adequate security protocols. The use of low-code tools for AI development, while accelerating innovation, may lead to insufficient testing and validation, increasing the risk of deploying agents that do not comply with security policies or regulatory requirements.

    To mitigate these risks, enterprises should adopt a comprehensive approach to AI governance. This includes implementing AI Security Posture Management (AISPM) programs that ensure ethical and trusted lifecycles for AI agents. Such programs should encompass data transparency, rigorous testing, and validation processes, as well as clear guidelines for the responsible use of AI technologies.

    In conclusion, while AI agents present a significant opportunity for business transformation, they also introduce complex challenges that require careful navigation. Organizations must balance the pursuit of innovation with the imperative of maintaining robust security and compliance frameworks to fully realize the benefits of AI integration.

    AI agent adoption is driving increases in opportunities, threats, and IT budgets

    While 79% of security leaders believe that AI agents will introduce new security and compliance challenges, 80% say AI agents will introduce new security opportunities.

    AI Agents in Action

    AI Governance: Applying AI Policy and Ethics through Principles and Assessments

    ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

    ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

    Businesses leveraging AI should prepare now for a future of increasing regulation.

    DISC InfoSec’s earlier posts on the AI topic

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    Tags: AI Agent, AI Agents in Action


    May 20 2025

    Steal Now, Crack Later: The Urgency of Quantum-Safe Security

    Category: Cyber resilience,Data encryptiondisc7 @ 8:29 am

    The security of traditional encryption hinges on the computational difficulty of solving prime number-based mathematical problems. These problems are so complex that, with today’s computing power, deciphering encrypted data by brute force—often referred to as “killing it with iron” (KIWI)—is practically impossible. This foundational challenge has kept data secure for decades, relying not on randomness but on insurmountable workload requirements.

    However, the landscape is changing rapidly with the emergence of quantum computing. Unlike classical machines, quantum computers are built for solving certain types of problems—like prime factorization—exponentially faster. This means encryption that’s currently unbreakable could soon become vulnerable. The concern isn’t theoretical; malicious actors are already collecting encrypted data, anticipating that future quantum capabilities will allow them to decrypt it later. This “steal now, crack later” approach makes today’s security obsolete in tomorrow’s quantum reality.

    As quantum computing advances, the urgency to adopt quantum-safe cryptography increases. Traditional systems need to evolve quickly to defend against this new class of threats. Organizations must prepare now by evaluating whether their current cryptographic infrastructure can withstand quantum-enabled attacks. Failure to act could result in critical exposure when quantum machines become operational at scale.

    Adaptability, compliance, and resilience are the new pillars of a secure, future-proof cybersecurity posture. This means not only upgrading encryption standards but also rethinking security architecture to ensure it can evolve with changing technologies. Organizations must consider how quickly and seamlessly they can shift to quantum-safe alternatives without disrupting business operations.

    Importantly, the way organizations view cybersecurity must also evolve. Many still treat security as a cost center, a necessary but burdensome investment. With the rise of generative AI and quantum computing, security should instead be seen as a value creator—a foundational component of digital trust, innovation, and competitive advantage. This mindset shift is crucial to justify the investments needed to transition into a quantum-safe future.

    Quantum computing is the next frontier. Sundar Pichai predicts that within 5 years, quantum will solve problems that classical computers can’t touch.

    Feedback:
    There is an urgent need for quantum-resilient security measures. The post successfully communicates technical risk without diving into complex math, which makes it accessible. My suggestion would be to expand slightly on practical next steps—like adopting post-quantum cryptographic algorithms (e.g., those recommended by NIST), running quantum-readiness assessments, and building awareness across leadership. Adding these elements would enhance the piece’s actionable value while reinforcing the central message.

    The shift to quantum-safe standards will take several years, as the standards continue to mature and vendors gradually adopt the new technologies. It’s important to take a flexible approach and be ready to update or replace cryptographic components as needed. Adopting a hybrid strategy—combining classical and quantum-safe algorithms—can help maintain compliance with existing requirements while introducing protection against future quantum threats.

    Quantum Computing and Information: A Scaffolding Approach

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    Tags: Quantum computing


    May 20 2025

    Why Legal Teams Should Lead AI Governance: Ivanti’s Cross-Functional Approach

    Category: AIdisc7 @ 8:25 am

    In a recent interview with Help Net Security, Brooke Johnson, Chief Legal Counsel and SVP of HR and Security at Ivanti, emphasized the critical role of legal departments in leading AI governance within organizations. She highlighted that unmanaged use of generative AI (GenAI) tools can introduce significant risks, including data privacy violations, algorithmic bias, and ethical concerns, particularly in sensitive areas like recruitment where flawed training data can lead to discriminatory outcomes.

    Johnson advocates for a cross-functional approach to AI governance, involving collaboration among legal, HR, IT, and security teams. This strategy aims to create clear, enforceable policies that enable responsible innovation without stifling progress. At Ivanti, such collaboration has led to the establishment of an AI Governance Council (AIGC), which oversees the safe and ethical use of AI tools by reviewing applications and providing guidance on acceptable use cases.

    Recognizing that a significant number of employees use GenAI tools without informing management, Johnson suggests that organizations should proactively assume AI is already in use. Legal teams should lead in defining safe usage parameters and provide practical training to employees, explaining the security implications and reasons behind certain restrictions.

    To ensure AI policies are effectively operationalized, Johnson recommends conducting assessments to identify current AI tool usage, developing clear and pragmatic policies, and offering vetted, secure platforms to reduce reliance on unsanctioned alternatives. She stresses that AI governance should be treated as a dynamic process, with policies evolving alongside technological advancements and emerging threats, maintained through ongoing cross-functional collaboration across departments and geographies.

    Why legal must lead on AI governance before it’s too late

    AI Governance: Applying AI Policy and Ethics through Principles and Assessments

    ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

    ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

    Businesses leveraging AI should prepare now for a future of increasing regulation.

    DISC InfoSec’s earlier posts on the AI topic

    InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services

    Tags: AI Governance, Ivanti


    May 19 2025

    AI Hallucinations Are Real—And They’re a Threat to Cybersecurity

    Category: AI,Cyber Threats,Threat detectiondisc7 @ 1:29 pm
    wildpixel/iStock via Getty Images

    AI hallucinations—instances where AI systems generate incorrect or misleading outputs—pose significant risks to cybersecurity operations. These errors can lead to the identification of non-existent vulnerabilities or misinterpretation of threat intelligence, resulting in unnecessary alerts and overlooked genuine threats. Such misdirections can divert resources from actual issues, creating new vulnerabilities and straining already limited Security Operations Center (SecOps) resources.

    A particularly concerning manifestation is “package hallucinations,” where AI models suggest non-existent software packages. Attackers can exploit this by creating malicious packages with these suggested names, a tactic known as “slopsquatting.” Developers, especially those less experienced, might inadvertently incorporate these harmful packages into their systems, introducing significant security risks.

    The over-reliance on AI-generated code without thorough verification exacerbates these risks. While senior developers might detect errors promptly, junior developers may lack the necessary skills to audit code effectively, increasing the likelihood of integrating flawed or malicious code into production environments. This dependency on AI outputs without proper validation can compromise system integrity.

    AI can also produce fabricated threat intelligence reports. If these are accepted without cross-verification, they can misguide security teams, causing them to focus on non-existent threats while real vulnerabilities remain unaddressed. This misallocation of attention can have severe consequences for organizational security.

    To mitigate these risks, experts recommend implementing structured trust frameworks around AI systems. This includes using middleware to vet AI inputs and outputs through deterministic checks and domain-specific filters, ensuring AI models operate within defined boundaries aligned with enterprise security needs.

    Traceability is another critical component. All AI-generated responses should include metadata detailing source context, model version, prompt structure, and timestamps. This information facilitates faster audits and root cause analyses when inaccuracies occur, enhancing accountability and control over AI outputs.

    Furthermore, employing Retrieval-Augmented Generation (RAG) can ground AI outputs in verified data sources, reducing the likelihood of hallucinations. Incorporating hallucination detection tools during testing phases and defining acceptable risk thresholds before deployment are also essential strategies. By embedding trust, traceability, and control into AI deployment, organizations can balance innovation with accountability, minimizing the operational impact of AI hallucinations.

    Source: AI hallucinations and their risk to cybersecurity operations

    Suggestions to counter AI hallucinations in cybersecurity operations:

    1. Human-in-the-loop (HITL): Always involve expert review for AI-generated outputs.
    2. Use Retrieval-Augmented Generation (RAG): Ground AI responses in verified, real-time data.
    3. Implement Guardrails: Apply domain-specific filters and deterministic rules to constrain outputs.
    4. Traceability: Log model version, prompts, and context for every AI response to aid audits.
    5. Test for Hallucinations: Include hallucination detection in model testing and validation pipelines.
    6. Set Risk Thresholds: Define acceptable error boundaries before deployment.
    7. Educate Users: Train users—especially junior staff—on verifying and validating AI outputs.
    8. Code Scanning Tools: Integrate static and dynamic code analysis tools to catch issues early.

    These steps can reduce reliance on AI alone and embed trust, verification, and control into its use.

    AI HALLUCINATION DEFENSE : Building Robust and Reliable Artificial Intelligence Systems

    Why GenAI SaaS is insecure and how to secure it

    Generative AI Security: Theories and Practices

    Step-by-Step: Build an Agent on AWS Bedrock

    From Oversight to Override: Enforcing AI Safety Through Infrastructure

    The Strategic Synergy: ISO 27001 and ISO 42001 – A New Era in Governance

    ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

    ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

    Businesses leveraging AI should prepare now for a future of increasing regulation.

    DISC InfoSec’s earlier posts on the AI topic

    InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services

    Tags: AI HALLUCINATION DEFENSE, AI Hallucinations


    May 18 2025

    Why GenAI SaaS is insecure and how to secure it

    Category: AI,Cloud computingdisc7 @ 8:54 am

    Many believe that Generative AI Software-as-a-Service (SaaS) tools, such as ChatGPT, are insecure because they train on user inputs and can retain data indefinitely. While these concerns are valid, there are ways to mitigate the risks, such as opting out, using enterprise versions, or implementing zero data retention (ZDR) policies. Self-hosting models also has its own challenges, such as cloud misconfigurations that can lead to data breaches.

    The key to addressing AI security concerns is to adopt a balanced, risk-based approach that considers security, compliance, privacy, and business needs. It is crucial to avoid overcompensating for SaaS risks by inadvertently turning your organization into a data center company.

    Another common myth is that organizations should start their AI program with security tools. While tools can be helpful, they should be implemented after establishing a solid foundation, such as maintaining an asset inventory, classifying data, and managing vendors.

    Some organizations believe that once they have an AI governance committee, their work is done. However, this is a misconception. Committees can be helpful if structured correctly, with clear decision authority, an established risk appetite, and hard limits on response times.

    If an AI governance committee turns into a debating club and cannot make decisions, it can hinder innovation. To avoid this, consider assigning AI risk management (but not ownership) to a single business unit before establishing a committee.

    It is essential to re-evaluate your beliefs about AI governance if they are not serving your organization effectively. Common mistakes companies make in this area will be discussed further in the future.

    GenAI is insecure because it trains on user inputs and can retain data indefinitely, posing risks to data privacy and security. To secure GenAI, organizations should adopt a balanced, risk-based approach that incorporates security, compliance, privacy, and business needs (AIMS). This can be achieved through measures such as opting out of data retention, using enterprise versions with enhanced security features, implementing zero data retention policies, or self-hosting models with proper cloud security configurations.

    Generative AI Security: Theories and Practices

    Step-by-Step: Build an Agent on AWS Bedrock

    From Oversight to Override: Enforcing AI Safety Through Infrastructure

    The Strategic Synergy: ISO 27001 and ISO 42001 – A New Era in Governance

    ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

    ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

    Businesses leveraging AI should prepare now for a future of increasing regulation.

    DISC InfoSec’s earlier posts on the AI topic

    InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services

    Tags: GenAI, Generative AI Security, InsecureGenAI, saas


    May 17 2025

    🔧 Step-by-Step: Build an Agent on AWS Bedrock

    Category: AI,Information Securitydisc7 @ 10:28 pm

    AWS diagram depicts a high-level architecture of this solution.

    1. Prerequisites

    • AWS account with access to Amazon Bedrock
    • IAM permissions to use Bedrock, Lambda (if using function calls), and optionally Amazon S3, DynamoDB, etc.
    • A foundation model enabled in your region (e.g., Claude, Titan, Mistral, etc.)

    2. Create a Bedrock Agent

    Go to the Amazon Bedrock Console > Agents.

    1. Create Agent
      • Name your agent.
      • Choose a foundation model (e.g., Claude 3 or Amazon Titan).
      • Add a brief description or instructions (this becomes part of the system prompt).
    2. Add Knowledge Bases (Optional)
      • Create or attach a knowledge base if you want RAG (retrieval augmented generation).
      • Can point to documents in S3 or other sources.
    3. Add Action Groups (for calling APIs)
      • Define an action group (e.g., “Check Order Status”).
      • Choose Lambda function or provide OpenAPI spec for the backend service.
      • Bedrock will automatically generate function-calling logic.
      • Test with sample input/output.
    4. Configure Agent Behavior
      • Define how the agent should respond, fallback handling, and if it can make external calls.

    3. Test the Agent

    • Use the Test Chat interface in the console.
    • Check:
      • Is the agent following instructions?
      • Are API calls being made when expected?
      • Is RAG retrieval working?

    4. Deploy the Agent

    1. Create an alias (like a version)
    2. Use the InvokeAgent API or integrate with your app via:
      • SDK (Boto3, JavaScript, etc.)
      • API Gateway + Lambda combo
      • Amazon Lex (for voice/chat interfaces)


    5. Monitor and Improve

    • Review logs in CloudWatch.
    • Fine-tune prompts or API integration as needed.
    • You can version prompts and knowledge base settings.

    🛡️ Use Case: AI Compliance Assistant for GRC Teams

    Goal

    Automate compliance queries, risk assessments, and control mapping using a Bedrock agent with knowledge base and API access.


    🔍 Scenario

    An enterprise GRC team wants an internal agent to:

    • Answer policy & framework questions (e.g., ISO 27001, NIST, SOC 2).
    • Map controls to compliance frameworks.
    • Summarize audit reports or findings.
    • Automate evidence collection from ticketing tools (e.g., JIRA, ServiceNow).
    • Respond to internal team queries (e.g., “What’s the risk rating for asset X?”).

    🔧 How to Build

    1. Foundation Model

    Use Anthropic Claude 3 (strong for reasoning and document analysis).

    2. Knowledge Base

    Load:

    • Security policies and procedures (PDFs, Word, CSV in S3).
    • Framework documentation mappings (ISO 27001 controls vs NIST CSF).
    • Audit logs, historical risk registers, previous assessments.

    3. Action Group (Optional)

    Integrate with:

    • JIRA API – pull compliance ticket status.
    • ServiceNow – fetch incident/evidence records.
    • Custom Lambda – query internal risk register or control catalog.

    4. System Prompt Example

    You are a compliance assistant for the InfoSec GRC team. 
    You help answer questions about controls, risks, frameworks, and policy alignment. 
    Always cite your source if available. If unsure, respond with "I need more context."
    

    💡 Sample User Prompts

    • “Map access control policies to NIST CSF.”
    • “What evidence do we have for control A.12.1.2?”
    • “List open compliance tasks from JIRA.”
    • “Summarize findings from the last SOC 2 audit.”

    🧩 What It Does

    The Bedrock Agent helps GRC teams and auditors by:

    1. Answering ISO 27001 control questions
      • “What’s required for A.12.4.1 – Event logging?”
      • “Do we need an anti-malware policy for A.12.2.1?”
    2. Mapping controls to internal policies or procedures
      • “Map A.13.2.1 to our remote access policy.”
    3. Fetching evidence from internal systems
      • Via Lambda/API to JIRA, Confluence, or SharePoint.
    4. Generating readiness assessments
      • Agent uses a questionnaire format to determine compliance status by engaging the user.
    5. Creating audit-ready reports
      • Summarizes what controls are implemented, partially implemented, or missing.

    🔗 Agent Architecture

    Components:

    • Foundation Model: Claude 3 on Bedrock (contextual QA and reasoning)
    • Knowledge Base:
      • ISO 27001 control descriptions
      • Your org’s InfoSec policies (in S3)
      • Control mappings (CSV or JSON in S3)
    • Action Group / Lambda:
      • Integrate with ticketing (JIRA)
      • Evidence retrieval
      • Risk register querying

    🗂️ Example Interaction

    User:
    “What controls address vendor management in ISO 27001?”

    Agent:
    “Clause A.15 covers supplier relationships. Specifically:

    • A.15.1.1 requires information security policy for supplier relationships.
    • A.15.2.2 requires monitoring and review of supplier services.

    Our ‘Third-Party Risk Management Policy’ maps to these controls. Would you like to see the last vendor assessment from JIRA?”

    🧠 Bonus: Prompt for the Agent

    You are an ISO 27001 compliance analyst. Your task is to help the GRC team interpret ISO controls, map them to our internal documents, and assist with evidence collection for audits. Be accurate and concise. If a control is not implemented, offer suggestions.

    What are the benefits of using AI agent in GRC field

    The use of AI agents in the Governance, Risk, and Compliance (GRC) field can provide several benefits, including:

    1. Automated Monitoring and Reporting: AI agents can continuously monitor various data sources, such as financial records, operational logs, and regulatory updates, to identify potential risks and compliance issues. This automated monitoring can help organizations stay up-to-date with changing regulations and promptly address any non-compliance or risk areas.
    2. Risk Analysis and Prediction: AI algorithms can analyze vast amounts of data and identify patterns that may indicate potential risks. By leveraging machine learning techniques, AI agents can predict future risks based on historical data and current trends, enabling organizations to take proactive measures to mitigate or prevent those risks.
    3. Compliance Management: AI agents can assist in interpreting and understanding complex regulatory requirements, ensuring that organizations remain compliant with relevant laws, regulations, and industry standards. They can also automate compliance processes, such as policy management, control testing, and reporting, reducing the risk of human error and increasing efficiency.
    4. Fraud Detection: AI algorithms can analyze transaction data, user behavior, and other relevant information to identify anomalies or patterns that may indicate fraudulent activities. This can help organizations detect and respond to fraud more quickly, minimizing financial losses and reputational damage.
    5. Continuous Improvement: AI agents can learn from the data they analyze and the actions taken by human experts, enabling them to continuously refine their models and improve their decision-making capabilities over time. This allows organizations to benefit from a constantly evolving and adapting GRC framework.
    6. Scalability and Efficiency: AI agents can process and analyze large volumes of data much faster and more efficiently than humans, enabling organizations to scale their GRC efforts without significantly increasing costs or resources.
    7. Centralized GRC Management: AI agents can provide a centralized platform for managing and integrating various GRC processes, enabling better collaboration, data sharing, and decision-making across different departments and functions within an organization.

    While AI agents can provide significant benefits in the GRC field, it is important to note that they should be used in combination with human expertise and oversight. AI should not be viewed as a complete replacement for human decision-making but rather as a powerful tool to augment and enhance GRC processes.

    What are the benefits of using AI agent in document management field

    The use of AI agents in the document management field can provide several benefits, including:

    1. Automated document classification and categorization: AI agents can analyze the content of documents and automatically classify them based on predefined rules or machine learning models, making it easier to organize and retrieve relevant information.
    2. Intelligent search and retrieval: AI agents can improve search capabilities by understanding the context and semantics of documents, enabling more accurate and relevant search results.
    3. Extraction of key information: AI agents can be trained to extract specific types of information from documents, such as dates, names, addresses, or key phrases, which can be useful for various business processes or analytics.
    4. Data entry automation: AI agents can be employed to automatically extract data from documents and populate fields in databases or other systems, reducing the need for manual data entry and minimizing errors.
    5. Intelligent document routing and workflow management: AI agents can analyze the content of documents and automatically route them to the appropriate individuals or departments based on predefined rules or workflows, streamlining business processes.
    6. Compliance and risk management: AI agents can be trained to identify sensitive or confidential information in documents and apply appropriate access controls or redaction measures, helping organizations comply with regulations and mitigate risks.
    7. Intelligent document summarization: AI agents can automatically generate summaries or abstracts of lengthy documents, saving time and effort for users who need to quickly understand the key points.
    8. Automatic language translation: AI agents can translate documents from one language to another, facilitating cross-language communication and collaboration.
    9. Improved user experience: AI agents can provide intelligent suggestions, contextual guidance, or virtual assistance to users, enhancing their experience with document management systems.

    Overall, the integration of AI agents in document management can lead to increased efficiency, improved accuracy, better organization, enhanced security, and more effective utilization of information resources within an organization.

    What are the benefits of using AI agent in merger and acquisition field

    The use of AI agents in the merger and acquisition (M&A) field can provide several benefits, including:

    1. Due diligence acceleration: AI agents can help streamline the due diligence process by rapidly analyzing large volumes of data, such as financial statements, contracts, and legal documents. This can help identify potential risks or opportunities more efficiently, saving time and resources.
    2. Target identification: AI algorithms can be trained to identify potential acquisition targets based on specific criteria, such as financial performance, market positioning, and strategic fit. This can help companies identify attractive targets more effectively and make informed decisions.
    3. Valuation analysis: AI agents can assist in valuing target companies by analyzing various financial and operational data points, as well as market trends and industry benchmarks. This can help companies make more accurate valuations and negotiate better deals.
    4. Integration planning: AI can be used to analyze the compatibility of systems, processes, and cultures between the acquiring and target companies. This can help identify potential integration challenges and develop strategies to address them, facilitating a smoother transition after the merger or acquisition.
    5. Synergy identification: AI algorithms can help identify potential synergies and cost-saving opportunities by analyzing data from both companies and identifying areas of overlap or complementarity. This can help maximize the value creation potential of the deal.
    6. Regulatory compliance: AI agents can assist in ensuring compliance with relevant regulations and laws during the M&A process by analyzing legal documents, contracts, and other relevant data.
    7. Predictive modeling: AI can be used to develop predictive models that estimate the potential outcomes and risks associated with a particular M&A transaction. This can help companies make more informed decisions and better manage risks.

    It’s important to note that while AI agents can provide valuable insights and support, human expertise and decision-making remain crucial in the M&A process. AI should be used as a complementary tool to augment and enhance the capabilities of M&A professionals, rather than as a complete replacement.

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