A staggering 91% of enterprises experienced software supply chain incidents in just one year, highlighting the urgent need for enhanced protections in continuous integration and continuous deployment (CI/CD) pipelines.
Four out of ten companies report that misconfigured cloud services, stolen secrets from source code repositories, insecure API usage, and compromised user credentials are increasingly common. The most frequent consequences of these attacks include the malicious introduction of crypto-jacking malware, affecting 43% of organizations, and remediation efforts that disrupt service level agreements (SLAs) for 41% of enterprises.
Among those impacted by software supply chain incidents in the past year, 96% faced some negative effect. Source: The Growing Complexity of Securing the Software Supply Chain, Enterprise Strategy Group.
Attackers are leveraging AI to refine their strategies and execute attacks faster than organizations can respond. As cybercriminals utilize offensive AI tactics, cybersecurity vendors must also adopt AI to enhance their defensive capabilities and remain competitive.
Why Software Supply Chains are Prime Targets
Software supply chains represent a high-value target for attackers, acting as a ransom multiplier. Nation-state actors, cybercrime groups, and advanced persistent threat (APT) entities often exploit software supply chain vulnerabilities, as this area has historically been poorly defended. Notable breaches include Okta, JetBrains, MOVEit, 3CX, and Kaseya VSA ransomware, which have affected countless businesses globally.
Five Ways AI Strengthens Supply Chain Security
The pace of the AI arms race is intensifying, particularly for organizations facing adversaries equipped with advanced generative AI tools. Fortunately, AI is emerging as a key player in detecting and mitigating intrusions targeting CI/CD pipelines. Here are five areas where AI is making an impact:
1. Cloud-Native Application Protection Platforms (CNAPP) automate security for hybrid and multicloud environments, embedding security measures early in the software development lifecycle (SDLC). CNAPPs with integrated AI and machine learning help DevSecOps teams identify threats proactively while examining code in repositories like GitHub before it is integrated into applications. Leading CNAPP providers include Cisco, CrowdStrike, and Trend Micro.
2. Endpoint Security benefits from AI's capability to secure identity levels. Cybercriminals often exploit privileged access credentials, moving between endpoints to launch broader attacks. AI can close gaps between identities and endpoints effectively.
3. Extended Detection and Response (XDR) platforms are advancing AI utilization. CrowdStrike’s CEO noted their development of algorithms that connect weak signals from various endpoints to deliver novel detections, now extending this capability to third-party partners. Leading XDR providers include Broadcom, Cisco, and Microsoft, enhancing their platforms with enriched telemetry data.
4. Automated Threat Detection develops over time through AI/ML models that learn from behavioral data. XDR and CNAPP vendors train their models using endpoint data to further improve automated detection capabilities, crucial for maintaining secure CI/CD pipelines.
5. Streamlined Analytics and Reporting across CI/CD pipelines use AI to identify potential risks or obstacles early while predicting attack patterns. The integration of a unified data lake with advanced analytics ensures that critical risks are prioritized, preserving software supply chain integrity.
AI and ML in Patch Management
Automating patch management, supported by diverse datasets, is another application of AI. Leading AI-powered patch management systems assess vulnerabilities and prioritize risks efficiently. Vendors like Ivanti and Tanium provide systems that not only address vulnerabilities but also streamline the overall patching process.
“Patching is not nearly as simple as it sounds,” states Srinivas Mukkamala, CPO of Ivanti. “Organizations must implement a risk-based patch management solution that automates vulnerability identification and prioritization to reduce risk without adding to workload.”
By leveraging AI and ML, enterprises can bolster their security posture in a rapidly evolving threat landscape, ensuring their software supply chains remain resilient against attacks.
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