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Showing posts with label AI Code Security. Show all posts
Rubrik
ai agents AI Code Security Anthropic code Guardian Cyber Security Rubrik

Rubrik Expands Project Hourglass to Bring AI-Powered Code Security to Enterprise

 



When Rubrik launched Project Hourglass at its FORWARD 2026 conference in Las Vegas back in June, the initiative set out to answer a question CISOs were already losing sleep over: what happens when an AI agent writing and deploying your code does something catastrophic and nobody can stop it in time?

On Thursday, at its GSI Summit in Goa, India, the cybersecurity firm announced the next step. Rubrik has expanded Project Hourglass to include a new tool called Rubrik Code Guardian, and has welcomed AHEAD, Trace3, and World Wide Technology (WWT) into the alliance, joining the six systems integrators that signed on at launch. The new capability is powered by Anthropic's Claude Mythos 5 and extends the alliance's reach from securing AI agents during execution to proactively identifying vulnerabilities in software code before deployment.


The Problem Driving All of This

Rubrik Zero Labs surveyed more than 1,600 IT and security leaders for its State of the Agent report and found that 86 percent expect AI agents to outpace their security guardrails within a year, while only 23 percent report full visibility into agents operating in their environments. More than 80 percent said agents require more manual oversight than the efficiency they save.

A separate Rubrik and Economist Enterprise study found 88 percent of enterprises experienced an AI agent security breach in 2026. The picture those numbers paint is one of organizations racing to deploy autonomous systems while the controls meant to govern them are still catching up.


What Code Guardian Does

The original Project Hourglass, which launched with Cognizant, Deloitte, LTM, HCLTech, NTT DATA, and Wipro as founding partners, focused on protecting AI agents at runtime through Rubrik Agent Cloud. That platform operates across three layers: Runtime Agent Security for behavioral guardrails and blast-radius control, Agent Rewind for fast repository recovery, and AI Context Guard for prompt integrity and control-plane protection.

Code Guardian shifts the security lens earlier in the development cycle. Rather than running against live production environments, the system tests a cloned, air-gapped copy of customer repositories. Claude Mythos 5 operates inside Rubrik's security harness to evaluate code against sophisticated, multi-step threat scenarios.

Three core capabilities define the product: isolated red-team analysis inside the air-gapped environment; attack chain discovery that reasons across files, services, identity roles, and cloud perimeters to find chained vulnerabilities that conventional static tools miss; and business impact prioritization that filters findings by actual exploitability and business criticality to reduce alert fatigue.

Alok Agrawal, Chief Solutions Officer at Rubrik, put the challenge plainly. "Engineering teams are turning to AI models to accelerate software delivery, but speed cannot compromise security or architectural integrity. By incorporating Rubrik Code Guardian into Project Hourglass, we are ensuring engineering teams are able to conduct red-team analysis, prioritize business impact and reduce risk."


The New Partners

Each of the three incoming partners brings a different angle to the coalition.

AHEAD, which builds enterprise technology architectures for large clients, framed the problem as one of inherited risk. Steven Sorensen, Specialty Solutions Engineer for Cyber Resiliency at AHEAD, said the goal is to get code attacked, tested, and validated in a safe environment before it ships, so teams can move faster knowing Rubrik's recovery capabilities sit underneath them as a safety net.

WWT's Chris Konrad, Vice President of Global Cyber, pointed to the company's Advanced Technology Center, where isolated threat testing has long been part of how it validates security solutions before recommending them. He said Code Guardian integrates naturally with that process.

Trace3 brought a perspective the others did not: its own teams have been running Rubrik Agent Cloud internally to protect their own agentic AI work before recommending it to clients. Sandy Salty, Chief Marketing Officer at Trace3, said that experience as both a user and a partner gives the firm a clearer view of what actually makes agentic environments more resilient at scale.


Where Things Stand

Rubrik Code Guardian is currently in private preview and accepting select design partners. It is not yet generally available and may change or be discontinued. Rubrik Agent Cloud, the original platform at the center of Project Hourglass, remains available to enterprise clients.

Dev Rishi, GM of AI at Rubrik, has previously described the core problem in stark terms: with AI agents, there is the potential for ten times the damage in one-tenth the time. That framing captures why the urgency behind Project Hourglass is unlikely to ease. As the volume of AI-generated code increases across enterprise environments, the window between a vulnerability being introduced and it being found by someone with bad intentions keeps getting smaller.



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Vulnerability Management
AI Code Security AI Coding Risks Cyber Security Dependency Security Open Source Security Remediation Debt Secure Software Development Software Supply Chain Security Vulnerability Management

Enterprise AI Coding Adoption Fuels Open-Source Security Debt


As artificial intelligence coding tools are rapidly adopted, development teams are able to develop software more quickly, however, cybersecurity teams are also becoming increasingly under pressure due to the increasing volume of AI-generated code. Open-source dependencies have become increasingly common, and their rapid introduction can result in increased vulnerabilities for an organization and additional remediation workloads. This is a key concern. 

A study involving 300 security and engineering leaders in technology, financial services, healthcare, manufacturing, and government sectors revealed the growing challenges associated with managing this risk. Organizations may face remediation debt if dependencies accumulate faster than security teams can assess and resolve them, which could adversely affect security operations, compliance, productivity, and overall business resilience. 

AI coding systems are expected to become increasingly autonomous as time goes on, resulting in software and dependencies being introduced into enterprise environments at a pace that existing security processes may not be able to keep up with. Prioritization is also becoming increasingly important in light of the growing number of vulnerabilities. If every CVE is treated equally, security resources will be consumed rapidly without addressing the weaknesses that are most significant. 

There are several factors that can be considered in determining which issues require immediate action, including whether the vulnerability is reachable, whether the flaw can be exploited, and which systems or data will be impacted. Security controls are also becoming more involved in software development earlier. Testing static applications for security and analysis of software composition can be incorporated into development environments and continuous integration/continuous delivery pipelines to identify vulnerable dependencies and insecure code before the release process progresses further. 

By following this approach, security teams are less likely to discover large volumes of unresolved issues when development has been completed. AI-assisted development is becoming increasingly reliant upon dependency visibility. 

Software bills of materials can provide a comprehensive inventory of components used across applications, while controlled package sources and dependency policies can prevent libraries with known vulnerabilities from entering the development environment. It is also necessary to clearly define who will be responsible for clearing this backlog. 

Creating remediation deadlines based on risk level and assigning vulnerabilities to development teams responsible for the affected code can assist in making the process more measurable. Creating and verifying tickets as well as completing fixes automatically can also contribute to preventing vulnerabilities from remaining unresolved after they have been identified. The security backlog itself is not the only challenge that AI-assisted development can present when engineers approve code generated without understanding the design or underlying assumptions. 

In the event that the generated code becomes part of critical application logic, such gaps in understanding can make it more difficult to make later changes and troubleshoot. Increasing volumes of artificial intelligence-assisted development are putting additional pressure on existing vulnerability management methods. 

Generated code may bring in open-source libraries and transitive dependencies that may not be examined as thoroughly as manually selected components. These dependencies can result in a steady buildup of vulnerabilities that require assessment and remediation, which may cause security teams to face a steadily increasing backlog. 

Counting CVEs alone cannot provide a reliable indication of actual risk. There may be more urgent urgency for issues with limited exposure when they affect application components, are exposed to untrusted input, or are associated with active exploitation. The business impact of an issue also plays a role in determining which findings should be addressed first, as well as which findings should be addressed first. 

Security validation is therefore becoming increasingly integrated into the development process. Identifying insecure code and vulnerable dependencies before they reach production can be accomplished by integrating static application security testing and software composition analysis into development workflows and Continuous Integration/Continuous Delivery pipelines. The implementation of this approach can reduce the amount of remediation work that must be done by security teams following deployment. 

Greater visibility into software components is also of importance. A software bill of materials can provide information about the open-source packages that are used across various applications, while controls around approved package sources can help prevent known vulnerable or unsuitable dependencies from entering development environments. Additionally, it is necessary to clearly define ownership of the remediation process itself. 

By assigning vulnerabilities to teams responsible for the affected code and establishing a risk-based remediation timeline, and verifying the fixes, organizations can prevent unresolved findings from building up as AI-driven development continues.

In order to ensure successful software development, organizations will need stronger visibility, risk-based vulnerability prioritization, and security controls throughout the development process. A strong focus on dependency management and remediation work can assist in preventing the growth of security debt from becoming a larger risk for the software supply chain.
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