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Smart Software Systems
What are cloud services and integrated software which can be load on hardware devices? Computing infrastructure, platforms and software are services business can pay for from the cloud. SaaS can be Google Docs or Microsoft Word, Github, Trello, Canva, Marvel, Dropbox and Slack these are tools for collaboration in real time for teams.
Cloud computing. Video on demand, software development tools, data mining solutions and virtual backup are examples of these tools. Repeatable and reliable systems depend on a high level of automation. Use tried and tested Open Source tools to deliver your solutions, contributing back to those communities where you can.
The online software marketplace is a place where connectivity is at it's heart. Development in React.js/Native mean on your mobile web (adaptive, responsive) data can be called from the back end to the end user. Progressive Web App, mobile app (native, hybrid) are based on APIs (RESTful/OData/JSON) and API design first.
For example and ecommerce platform would connect with a payment solution with a sure call the a specialist service provider that can handle the transaction. API Management Platform (e.g. Mulesoft, Dell Boomi, Oracle Fusion, MS Biztalk, Google/Apigee, Software AG, AWS Gateway, IBM API Connect) are in many software solutions from the root architecture. For example CMS platforms: Drupal, Oracle, Liferay, Contentful incorporate is in their latest versions.
All these software suppliers can be a game changer however they can also be a risk. Consider the following practises to stay on top of technological changes and new cyber threats.
DevSecOps practises
DevSecOps is an approach that integrates security practices into the DevOps process, involving collaboration between the development team, operations team, and security team. By embedding security into every stage of the software development lifecycle, from initial planning through deployment and maintenance, DevSecOps ensures that security is a shared responsibility. The development team focuses on building secure code, the operations team ensures secure and reliable infrastructure, and the security team provides guidance, tools and oversight to identify and mitigate potential vulnerabilities. This collaboration enhances the overall security posture, reduces risks and delivers more secure software faster.
MLSecOps practises
MLSecOps is a flexible framework that complements the work of the NIST AI RMF (Risk Management Framework) and focuses on securing the entire machine learning pipeline. This includes data collection, model development, deployment and monitoring to safeguard ML assets in production environments. Key threats to AI and ML systems include supply chain vulnerabilities, prompt injection attacks, backdoor attacks, evasion attacks, model extraction, and poisoning attacks. By integrating security practices throughout the AI/ML lifecycle, organizations can build resilient systems designed to handle these unique challenges.
AI and ML systems are increasingly being used in various applications, from customer support chatbots to robo-advisors for stock trading. However, these systems can fail due to attacks or vulnerabilities, leading to technological, operational, and reputational damages. It's essential to understand AI use cases, recognize vulnerabilities, assess risks, and implement mitigation strategies. The MLSecOps framework supports these efforts by focusing on supply chain vulnerabilities, model provenance, governance, risk and compliance, trusted AI, and adversarial ML. Implementing MLSecOps involves cross-functional ML security teams, training, security tools, policy as code, privacy and data security considerations and continuous monitoring.
Consider secure AI software solutions.
AI software solution for protecting your business against cyber threats helping small businesses safeguard their digital assets and maintain a secure environment. Software as a service SaaS.
AI Software Solutions for Protecting Small Businesses
Enterprises in all industries are starting to see how powerful AI/ML can be. How is AI be used in security?
- SentinelOne: Known for its advanced threat-hunting and incident response capabilities, SentinelOne uses AI and machine learning to provide real-time prevention, detection, and threat hunting across user endpoints, containers, cloud workloads, and IoT devices.
- CrowdStrike: This solution focuses on monitoring user endpoint behaviour. It uses AI to detect and respond to threats, providing comprehensive endpoint protection.
- Vectra AI: Vectra AI specializes in hybrid attack detection, investigation, and response. It uses AI to identify and mitigate threats across both on-premise and cloud environments.
- Darktrace: Darktrace is known for its ability to neutralize novel threats. It uses AI to detect anomalies and respond autonomously to potential cyber threats.
- Fortinet: Fortinet is designed to prevent zero-day threats. It uses AI to analyse and respond to new and emerging threats, providing robust security for small businesses.
AI attack and defence
Is your businesses developing, deploying and utilising AI applications? Today's AI threats include prompt injections, DoS attacks and data leakage. The below are links of organisations tasked with making you aware of AI and security.