DISC InfoSec blog

DISC InfoSec’s earlier posts on the AI topic

Tags: AI Agents, AI Agents in Action

Jun 09 2025

Why WPS Office Is a Smart Microsoft Office Alternative for Individuals and Small Businesses

Category: Information Security — disc7 @ 10:55 am

If you prefer not to use Microsoft Office in the U.S., you can try WPS Office instead, which is a free alternative offering many of the same features.

For users who do not wish to use Microsoft Office, WPS Office is a strong alternative worth considering. It’s a free office suite compatible with Word, Excel, and PowerPoint files, and offers a user-friendly interface along with cloud integration, PDF tools, and cross-platform support. It’s especially useful for individuals or small businesses looking to cut software costs without sacrificing essential functionality.

If you don’t want to use Microsoft Office, consider WPS Office — a free, lightweight, and fully compatible alternative. It’s ideal for individual users and small businesses (SMBs) who need powerful tools without the high licensing cost. WPS Office supports Word, Excel, and PowerPoint formats, and includes PDF editing, cloud storage integration, and templates for everyday business tasks. Its clean interface, cross-platform availability (Windows, macOS, Linux, Android, iOS), and low system requirements make it a great fit for teams working remotely or on a budget.

WPSOffice #MicrosoftOfficeAlternative #FreeOfficeSuite #SmallBusinessTools #ProductivitySoftware #CrossPlatform #PDFEditor #BudgetFriendly #OfficeApps #SMBTech

Tags: Microsoft Office Alternative, WPS Office

Jun 08 2025

Top 10 Most Used Tools in Kali Linux & KaliGPT

Category: Hacking,Information Security,Linux Security,Security Tools — disc7 @ 11:16 am

🔟 Top 10 Most Used Tools in Kali Linux

Tool	Purpose	Typical Use Case
1. Nmap	Network Scanning & Enumeration	Host discovery, port scanning, OS/service detection
2. Metasploit Framework	Exploitation Framework	Exploit known vulnerabilities, create payloads
3. Wireshark	Network Traffic Analysis	Capture and analyze network packets
4. Burp Suite	Web Application Testing	Intercept & modify HTTP/S traffic, scan for web vulns
5. Aircrack-ng	Wireless Security Testing	Cracking Wi-Fi passwords, sniffing wireless traffic
6. Hydra	Brute-Force Password Cracking	Cracks login credentials (SSH, FTP, etc.)
7. John the Ripper	Password Cracker	Offline cracking of hashed passwords
8. sqlmap	SQL Injection Automation	Detect and exploit SQL injection flaws
9. Nikto	Web Server Scanner	Scan for web server misconfigurations & vulns
10. Netcat (nc)	Network Utility	Debugging, banner grabbing, simple backdoors

KaliGPT: Revolutionizing Cybersecurity With AI-Powered Intelligence In Kali Linux

Kali GPT doesn’t just support one set number of tools — it integrates deeply with all tools available in the Kali Linux ecosystem, which currently includes over 600 pre-installed security tools in the official Kali repositories – If it’s on Kali, Kali GPT supports it…

Kali GPT isn’t just an AI assistant — it’s a next-gen cybersecurity learning engine. For students aiming to enter the fields of ethical hacking, penetration testing, or digital forensics, here’s why Kali GPT is your ultimate study companion.

🧠 1. Learn by Doing, Not Just Reading

Kali GPT promotes hands-on, interactive learning, guiding students through:

Setting up Kali Linux environments (VMs, NetHunter, cloud)
Running and understanding real tools like Nmap, Wireshark, Metasploit
Simulating real-world attack scenarios (MITRE ATT&CK-based)
Building labs with targets like Metasploitable, Juice Shop, DVWA

This turns passive theory into active skill development.

In today’s rapidly changing cybersecurity landscape, staying ahead of threats demands more than just cutting-edge tools—it requires smart, real-time guidance.

Kali GPT is an AI assistant based on the GPT-4 architecture and is integrated with Kali Linux to support offensive security professionals and students. This groundbreaking tool marks a new era in penetration testing, acting as an intelligent co-pilot that redefines the cybersecurity workflow.

This new tool provides intelligent automation and real-time assistance. It can generate payloads, explain tools like Metasploit and Nmap, and recommend appropriate exploits—all directly within the terminal.

Key Features

Interactive Learning: Kali GPT acts as a tutor, guiding users through various cybersecurity tools and techniques. For example, if you want to master Metasploit, Kali GPT provides clear, step-by-step instructions, explanations, and best practices to accelerate your learning.
Real-Time Troubleshooting: Facing issues like a failed Nmap scan? Kali GPT diagnoses the problem, offers possible reasons, and suggests solutions to keep your tasks running smoothly.
Command Generation: Need a Linux command tailored to a specific task? Simply ask Kali GPT, such as “How can I find all files larger than 100MB in a directory?” and it will generate the precise command you need.
Seamless Tool Integration: Kali GPT connects directly with Kali Linux tools, enabling users to execute commands and receive feedback right within the interface—streamlining workflows and increasing productivity.

🐉 Kali GPT’s methodology is primarily influenced by a synthesis of industry-proven methodologies and elite-level documentation, including:

📚 Key Source Methodologies & Influences

🔺 MITRE ATT&CK Framework
- Used for mapping tactics, techniques, and procedures (TTPs).
- Integrated throughout Kali GPT’s threat modeling and adversary emulation logic.
📕 Advanced Security Testing with Kali Linux by Daniel Dieterle
- Directly referenced in your uploaded file.
- Offers practical hands-on walkthroughs with real-world lab setups.
- Emphasizes tool-based learning over theory — a core trait in Kali GPT’s interactive approach.
📘 Penetration Testing: A Hands-On Introduction to Hacking by Georgia Weidman
- Influences Kali GPT’s baseline for beginner-to-intermediate structured offensive testing.
- Known for lab realism and methodical vulnerability exploitation.
🛡️ Red Team Field Manual (RTFM) & Blue Team Field Manual (BTFM)
- Inform command-line fluency, post-exploitation routines, and red team practices.
📙 The Hacker Playbook Series by Peter Kim
- A tactical source for step-by-step attack paths, including recon, exploitation, privilege escalation, and pivoting.
📗 Kali Linux Official Documentation & Offensive Security Materials
- Supports tool syntax, metapackage management, update flows, and usage ethics.
- Offensive Security’s PWK/OSCP methodologies play a major role in scenario planning.

Tags: Kali Linux, KaliGPT

AI Value Creators: Beyond the Generative AI User Mindset

Jun 03 2025

IBM’s model-routing approach

Category: AI — disc7 @ 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)

Request Input: A user sends a prompt (e.g., a question or task).
Router Evaluation: The orchestrator examines the request’s content—this might involve analyzing intent, complexity, or topic (e.g., legal vs. creative writing).
Model Selection: Based on predefined rules, statistical learning, or even another ML model, the router selects the optimal model from a pool.
Forwarding & Inference: The request is forwarded to the chosen model, which generates the response.
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.

Tags: IBM model-routing

Comments (14)

Jun 03 2025

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

Category: Information Security,Phishing — disc7 @ 12:16 pm

🔟 Phishing Tips:

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

DISC-49-Compliance-self-assessment Download

Tags: phishing

Protecting Yourself: Stay Away from AI Scams

Jun 03 2025

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

Category: AI,Security Awareness — disc7 @ 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.

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

Comments (16)

Jun 02 2025

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

Category: AI,CISO,Information Security,vCISO — disc7 @ 5:12 pm

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.
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.
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.
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.
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

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.
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.
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.
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.
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.
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.

A new ISO standard, ISO 42005 provides organizations a structured, actionable pathway to assess and document AI risks, benefits, and alignment with global compliance frameworks.

A New Era in Governance

The CISO 3.0: A Guide to Next-Generation Cybersecurity Leadership

Interpretation of Ethical AI Deployment under the EU AI Act

AI in the Workplace: Replacing Tasks, Not People

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.

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

DISC InfoSec’s earlier posts on the AI topic

Tags: AI Governance, CISO 3.0

Comments (18)

Jun 01 2025

AI in the Workplace: Replacing Tasks, Not People

Category: AI — disc7 @ 3:48 pm

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.
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.
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.
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.
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.
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.

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”

DATA RESIDENT : AN ADVANCED APPROACH TO DATA QUALITY, PROVENANCE, AND CONTINUITY IN DYNAMIC ENVIRONMENTS

Interpretation of Ethical AI Deployment under the EU AI Act

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.

Securing the AWS Cloud: A Guide for Learning to Secure AWS Infrastructure (Tech Today)

DISC InfoSec’s earlier posts on the AI topic

Tags: AI Governance

Comments (14)

May 30 2025

How Cybersecurity Experts Are Strengthening Defenses with AWS Tools

Category: AWS Security,cyber security,Security Tools — disc7 @ 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

DATA RESIDENT : AN ADVANCED APPROACH TO DATA QUALITY, PROVENANCE, AND CONTINUITY IN DYNAMIC ENVIRONMENTS

May 29 2025

Why CISOs Must Prioritize Data Provenance in AI Governance

Category: AI,IT Governance — disc7 @ 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

Interpretation of Ethical AI Deployment under the EU AI Act

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.

What is an Amazon Bedrock

DISC InfoSec’s earlier posts on the AI topic

Tags: data provenance

Comments (16)

May 28 2025

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

Category: AWS Security,GRC — disc7 @ 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:

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:

Choose appropriate foundation models available through Bedrock
Fine-tune these models with domain-specific data if necessary
Develop applications that integrate with Bedrock’s API
Implement proper security and access controls
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:

Data Collection:
- Gather relevant data from various sources (financial reports, operational metrics, incident reports, external market data, etc.)
- Ensure data quality and completeness
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)
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
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
Trend Analysis:
- Analyze historical data to identify trends over time
- Look for patterns that might indicate emerging risks
Predictive Modeling:
- Use techniques like regression analysis or machine learning to predict future risks
- Develop models that can forecast potential risk scenarios
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
Data Visualization:
- Create visual representations of the data (charts, graphs, heat maps)
- Use dashboards to provide an overview of key risk indicators
Text Analysis:
- If dealing with unstructured data (like customer complaints or social media), use natural language processing techniques to extract insights
Risk Mapping:
- Map identified risks to business processes or objectives
- Assess the potential impact and likelihood of each risk
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
Interdependency Analysis:
- Identify connections between different risks
- Assess how risks might compound or trigger each other
Continuous Monitoring:
- Set up systems for real-time or near-real-time risk monitoring
- Establish alerts for when key risk indicators exceed predefined thresholds
Expert Review:
- Have subject matter experts review the analysis results
- Incorporate qualitative insights to complement the data-driven analysis
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.

Generative AI with Amazon Bedrock: Build, scale, and secure generative AI applications using Amazon Bedrock

Amazon Bedrock Agents in Practice: Real-World Applications and Case Studies

DISC InfoSec vCISO Services

ISO 27k Compliance, Audit and Certification

Tags: Amazon Bedrock, Amazon Bedrock Agents, AWS

DISC InfoSec vCISO Services

May 24 2025

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

Category: Information Security,Security Compliance,vCISO — disc7 @ 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

Strengths	Weaknesses
Deep expertise in InfoSec & compliance	May lack scalability without automation
Custom vCISO engagements	High-touch model limits price elasticity

Opportunities	Threats
Demand surge in SMBs & startups	Commoditization 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.)

ISO 27k Compliance, Audit and Certification

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

Tags: Information Security Compliance, vCISO

Comments (1)

May 23 2025

Interpretation of Ethical AI Deployment under the EU AI Act

Category: AI — disc7 @ 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.

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

Tags: Digital Ethics, EU AI Act, ISO 42001

Comments (17)

May 22 2025

AI Data Security Report

Category: AI,data security — disc7 @ 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:

Data Supply Chain Vulnerabilities: Risks associated with sourcing data from external providers, which may introduce compromised or malicious datasets.
Poisoned Data: The intentional insertion of malicious data into training datasets to manipulate AI behavior.
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

Tags: AI Data Security

Artificial Intelligence: Legal Issues, Policy, and Practical Strategies

May 22 2025

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

Category: AI,Security and privacy Law — disc7 @ 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

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

How a Spy in Our Pocket Threatens the End of Privacy

May 21 2025

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

Category: Spyware — disc7 @ 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.

Tags: NSO Group, Pegasus

May 21 2025

8 domains of CISSP

Category: CISSP,Information Security — disc7 @ 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 domain	Current weighting (effective 1 May 2021)	Revised weighting (effective 15 April 2024)
1. Security and Risk Management	15%	16%
2. Asset Security	10%	10%
3. Security Architecture and Engineering	13%	13%
4. Communication and Network Security	13%	13%
5. Identity and Access Management (IAM)	13%	13%
6. Security Assessment and Testing	12%	12%
7. Security Operations	13%	13%
8. Software Development Security	11%	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

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

May 20 2025

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

Category: AI — disc7 @ 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

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

Tags: AI Agent, AI Agents in Action

Quantum Computing and Information: A Scaffolding Approach

May 20 2025

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

Category: Cyber resilience,Data encryption — disc7 @ 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.

Tags: Quantum computing

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

May 20 2025

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

Category: AI — disc7 @ 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.

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

Tags: AI Governance, Ivanti