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2025-02-14
On February 7, 2025, the National Cybersecurity Agency for France (ANSSI) published a joint high-level risk analysis report on artificial intelligence (AI) titled Building Trust in AI Through a Cyber Risk-Based Approach. This report, developed in collaboration with CERT-In and other national authorities, emphasizes the importance of a risk-based approach to secure AI systems and value chains. It also highlights the need for discussions on AI-related cyber risks and mitigation strategies to foster trusted AI development.
The report references two key documents published by CERT-In:
1. Technical Guidelines on Software Bill of Materials (SBOM)
Available at: https://lnkd.in/eMbyrnky
2. API Security: Threats, Best Practices, Challenges, and Way Forward Using AI
Available at: https://lnkd.in/enuUX6xX
Additionally, the following resources were shared:
Practice-Verified Codes and Commands
To implement a risk-based approach in securing AI systems, here are some practical commands and tools:
Linux Commands for Cybersecurity
1. Audit System Logs for Suspicious Activity
sudo grep "FAILED LOGIN" /var/log/auth.log
This command helps identify failed login attempts, which could indicate brute force attacks.
2. Check Open Ports and Services
sudo netstat -tuln
Use this to identify open ports and services that might be vulnerable to exploitation.
3. Monitor Network Traffic
sudo tcpdump -i eth0 -n
This command captures network traffic on the `eth0` interface, useful for detecting unusual activity.
Windows Commands for Cybersecurity
1. Check Active Connections
netstat -an
This command displays active connections and listening ports.
2. Scan for Malware Using Windows Defender
Start-MpScan -ScanType FullScan
Initiates a full system scan using Windows Defender.
3. Audit User Account Activities
Get-EventLog -LogName Security -InstanceId 4624
Retrieves successful login events from the Security log.
What Undercode Say
The report Building Trust in AI Through a Cyber Risk-Based Approach underscores the critical need for a structured, risk-based methodology to secure AI systems. As AI continues to integrate into critical infrastructure, the potential cyber risks grow exponentially. The collaboration between ANSSI and CERT-In highlights the global nature of these challenges and the importance of international cooperation.
To mitigate AI-related cyber risks, organizations must adopt robust security practices. For instance, implementing a Software Bill of Materials (SBOM) ensures transparency in software components, reducing vulnerabilities. Similarly, securing APIs with AI-driven threat detection can prevent data breaches and unauthorized access.
On a technical level, Linux and Windows systems offer powerful tools for monitoring and securing AI environments. Commands like netstat, tcpdump, and `Get-EventLog` provide real-time insights into system activities, enabling proactive threat detection. Additionally, regular audits and scans, as demonstrated by the `Start-MpScan` command, are essential for maintaining system integrity.
For further reading, refer to the CERT-In documents on SBOM (https://lnkd.in/eMbyrnky) and API security (https://lnkd.in/enuUX6xX). These resources provide in-depth guidance on securing AI systems and mitigating cyber risks.
In conclusion, the future of AI depends on our ability to build trust through cybersecurity. By leveraging risk-based approaches, international collaboration, and robust technical practices, we can ensure the safe and secure development of AI technologies.
References:
Hackers Feeds, Undercode AI
