We offer set of trainings. Click + sign in front of specific item below to open it and see detailed trainings content bellow (preliminary).
- Define and implement Software Security Assurance (SSA) program in your company
- Define Application and Software Security practice and ISMS (policies, standards, processes, guidance, tools)
- Integrate security into your Software Development Lifecycle (SDLC)
- Automate specific parts of process
- Measure effectiveness and KPIs
- Secure Development Trainings
- Integrations with other security tools (such are GRC, SOAR and similar tools)
Specific parts of Application Security practice:
- Defining security requirements
- Security architecture
- Application security risk management and compliance
- Threat modeling
- Application Security Testing
- SAST – Static Application Security Testing
- SCA – Software Composition Analysis
- IAST – Interactive Application Security Testing
- Secrets Scanning
- Container scanning
- Configuration and environment hardening
- API Security Testing
- IaC – Infrastructure as Code
- DAST – Dynamic Application Security Testing
- RASP – Runtime Application Self-Protection
- Application Security Monitoring
- Vulnerabilities Assessment
- Penetration Testing
We also will show Glog.AI as an integral part of DevSecOps which helps to make software more secure in Software Development Lifecycle (SDLC). See also Glog.AI Products and our Services.
- Introduction
- Demystifying artificial intelligence for cybersecurity
- Cybersecurity and software security goals, frameworks and gaps
- An approach to solving cybersecurity challenges with AI
- Applying machine learning to cybersecurity
- Practical considerations, risks and limitations
- How to start and implement a successful AI based security project
- How to choose AI based security products
Second part:
- How AI can be misused for performing cybersecurity attacks
- Use AI to defend information systems, software and networks
- How ML and AI can help with network and endpoint security, threat intelligence, software/application security and security predictions
- Real life examples of cybersecurity and software security solutions aided by ML & AI
- Future concerns and opportunities related to AI
- Conclusions
Benefits we have achieved by use of machine learning (ML) and artificial intelligence (AI) to improve cybersecurity and software security, will be covered through real active projects, products, solutions and services.
There are millions of interesting events from a security perspective annually in an average company or organization. Humans hardly can cope with all of them and breaches cost a lot in money, reputation and other costs and damages. Particular challenges are: false positives generated by tools on the market at present time, noise triage and how to remediate/fix issues.
Solutions and case studies for network and endpoint security, threat intelligence and predictions, as well as software security including false positives reduction and remediation of security vulnerabilities in software code, with possibility to achieve even automatic remediation will be presented based on active projects. Solutions can be either based on cloud or on premise and applied from small and medium companies to big enterprises and organizations.
These solutions offer high accuracy, fast detection and remediation, as well as cost and resources savings as they are based on modern technology and predictive approach. Solutions are implemented through real life projects: Software Security – Glog.AI, Network [& End-point] Security – INPRESEC (Intelligent Predictive Security), Threat Intelligence – Security Predictions, Virtual Security Operations Center – vSOC.
- Strategic Landscape: Examines the 2025 AI ecosystem, including the rise of autonomous agents and open-weights risks, against the backdrop of global innovation and regulation.
- Universal Risks: Identifies platform-independent threats such as data leakage, hallucinations, and copyright infringement that compromise business integrity regardless of the specific tool used.
- Cybersecurity Battleground: Highlights the escalating arms race where attackers leverage AI for polymorphic malware and deepfakes, compelling defenders to adopt predictive AI countermeasures.
- Secure Development: Demonstrates how AI tools can accelerate vulnerability remediation and enforce secure coding practices better than traditional methods.
- AI-Enhanced Threat Intelligence: Demonstrates how ML algorithms process massive datasets to identify genuine threats, predict attack probabilities, and find the “needle in the haystack” amidst noise.
- Network Anomaly Detection: details the use of AI agents and sensors to monitor network behavior in real-time, instantly flagging anomalies and potential threats at both the network and end-point levels.
- Regulatory Compliance: Navigates the EU AI Act’s risk-based framework and global ethical principles to ensure lawful, robust, and human-centric AI deployment.
- Stealth Usage: Addresses the prevalence of Shadow AI and decentralized purchasing, offering strategies to transition from blanket bans to managed, secure access.
- Operational Transformation: Explores the future of work where AI functions as a strategic assistant, automating routine tasks to elevate human roles to high-value, creative problem solving.
- Universal Reach: Addresses unauthorized AI usage currently affecting 98% of organizations.
- Financial Risk: Highlights the increased breach costs linked to extensive Shadow AI usage.
- Critical Threats: Covers data leakage, IP exposure, and compliance violations like GDPR and HIPAA.
- Stealth Factors: Explains why Shadow AI evades traditional IT detection by hiding in personal accounts and approved apps.
- Actionable Governance: Provides strategies for policy creation, technical controls, and employee training to manage risk without stifling innovation.
- Widespread Impact: Addresses the prevalence of hallucination errors affecting critical sectors, including legal services, healthcare, and customer support systems.
- Business & Legal Liability: detailed analysis of the direct risks, from court sanctions for fabricated citations to corporate liability for incorrect chatbot advice.
- Operational Integrity: Identifies how “factuality” and “faithfulness” errors erode trust by generating plausible but completely false statistics, events, and medical records.
- Deceptive Confidence: Explains the technical root causes where models prioritize fluency over accuracy, creating convincing “guesses” that mask uncertainty.
- Strategic Mitigation: Outlines essential defense mechanisms, including Retrieval-Augmented Generation (RAG), human-in-the-loop verification, and calibration-first evaluation.
- Inadvertent Exposure: Addresses how employees seeking productivity gains accidentally leak confidential source code, meeting transcripts, and product specs through standard prompts and uploads.
- The Retention Trap: Explains the opacity of public AI data lifecycles, where vendor logging and model training retention can permanently expose proprietary information to competitors.
- Regulatory & Legal Fallout: Details the severe consequences of sharing PII or regulated data, including GDPR fines, HIPAA violations, and immediate breach of contract for exposing NDA-protected terms.
- Competitive Compromise: Highlights the strategic risks of leaking business roadmaps and intellectual property, which can undermine market advantage and expose the organization to supply-chain vulnerabilities.
- Defensive Governance: Outlines “Do’s and Don’ts” for safe adoption, shifting from blanket bans to managed access via enterprise-grade tools, Single Sign-On (SSO), and data anonymization.
- Ethical Foundation: Establishes a unified standard for Trustworthy AI that is lawful, robust, and human-centric, ensuring alignment with global frameworks like the EU AI Act and OECD principles.
- Regulatory Compliance: Navigates the complex legal landscape using a risk-based pyramid approach, distinguishing between prohibited, high-risk, and minimal-risk systems to determine necessary oversight.
- Governance Structure: Defines a clear internal hierarchy involving an AI Governance Council and Ethics Officers to oversee mandatory risk assessments (ARIA) and fundamental rights impact assessments (FRIA).
- Safe Usage Protocols: Enforces strict prohibitions on sharing confidential corporate data or PII with public Generative AI tools while mandating human verification of all AI outputs.
- IP & Copyright Protection: Clarifies intellectual property rights by emphasizing human authorship requirements for copyright and managing vendor licensing terms to protect trade secrets.
Feel free to contact us for help.