Photo Courtesy of: Scribe Security
The adoption of AI assistants such as GitHub Copilot has transformed software development. Analysts estimate that by 2025, more than 50% of new code will be machine-generated, according to Gartner. While this has accelerated productivity, it has also created new security challenges. Automated suggestions often contain misconfigurations, exposed secrets, or unpatched vulnerabilities.
For companies managing thousands of repositories and dependencies, this flood of AI-produced code expands an already complex attack surface. A 2024 report from IBM found that the average cost of a data breach reached $4.45 million, with supply chain vulnerabilities cited as one of the fastest-growing sources of exposure. Governments in the United States, European Union, and Japan have introduced stricter requirements for verifiable software provenance, pressing companies to adopt security mechanisms that can track the origin and integrity of every artifact.
“Development speed has accelerated beyond the reach of manual inspection,” said Rubi Arbel, CEO of Scribe Security. “Organizations need systems that validate and record every step, whether code is written by humans or generated by AI.”
Continuous Evidence and Policy Guardrails
Scribe Security has built its ScribeHub platform around the idea that security cannot be a one-time check. Instead of relying solely on periodic scans,ScribeHub collects signed evidence during each phase of the software development lifecycle. This includes Software Bills of Materials (SBOMs), scanner results, and provenance records, all transformed into machine-readable attestations.
The evidence is stored in a tamper-proof knowledge graph linking artifacts, identities, and actions. This allows security teams to trace vulnerabilities back to specific commits, tools, or configurations. Policies written as code act as guardrails, blocking unverified or tampered artifacts from progressing to deployment. Developers see the impact through contextual feedback, with the option to trigger automated fixes without leaving their workflows.
This method has gained traction among regulated industries, where compliance frameworks such as SLSA, NIST SSDF, and the EU’s Cyber Resilience Act require proof that software components meet defined security standards. By embedding checks early and maintaining cryptographic records, organizations reduce the risk of last-minute failures or audit deficiencies.
Agentic Security: AI for Remediation
The introduction of AI-generated code prompted Scribe Security to add a new dimension: AI-driven remediation. Rather than bolting algorithms onto dashboards, the company designed a network of task-specific agents that interact with the signed knowledge graph. Each agent specializes in a function - triaging vulnerabilities, generating secure patches, analyzing Dockerfiles, or drafting compliance reports.
One example is “Remus,” an agentic workflow capable of producing pull requests to patch insecure dependencies or configuration files. Another, “Compy,” continuously compares signed evidence against standards such as PCI DSS or FedRAMP, producing audit-ready documentation as part of everyday development. These functions are designed to reduce the time between detection and resolution, cutting what once took weeks into hours.
“AI-generated fixes only work if they are tied to verified evidence,” Arbel explained. “By combining signed provenance with automated remediation, we reduce both human workload and the likelihood of introducing new errors.”
Implications for CISOs and Developers
Chief Information Security Officers face mounting pressure to reconcile speed with accountability. In many organizations, security staff are outnumbered by developers at ratios as high as 1 to 100. Without automation, investigating alerts and preparing for audits can overwhelm teams. According to a 2025 survey by ISACA, 66% of companies reported difficulty filling cybersecurity roles, particularly in software supply chain security.
For developers, traditional security processes are often experienced as bottlenecks. By integrating evidence collection and agent-driven remediation directly into the tools they already use, friction is reduced. Pull requests arrive with explanations, policies are enforced consistently, and compliance reports are generated continuously. The result is a development cycle that moves quickly without bypassing security requirements.
Market Forces and Regulatory Drivers
The cybersecurity market is projected to grow from $262 billion in 2025 to more than $350 billion by 2030, with software supply chain security cited as one of the highest-priority investment areas. In the U.S., Executive Order 14028, 14144, and NIST SSDF mandate federal contractors to produce SBOMs, while Europe’s Cyber Resilience Act introduces similar requirements for technology vendors. Japan’s government has also advanced legislation to enhance monitoring of supply chain risks, with implementation deadlines approaching by 2027.
These regulatory pressures align with the increasing prevalence of AI in development. Companies can no longer rely on ad hoc fixes; they must demonstrate continuous verification of their software factories. Solutions that combine cryptographic integrity checks, evidence trails, and AI remediation are quickly moving from optional to necessary.
“Trust in software now depends on being able to prove how it was built,” Arbel said. “That proof must be generated at the same speed as the code itself.”