Insights

Global Infosec Award from Cyber Defense Magazine in 2026.

Zero Trust for Code Starts With Understanding Intent

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The software supply chain has become one of the most targeted attack surfaces in modern security. As organizations rely more heavily on third-party components, open-source libraries, and automated CI/CD pipelines, attackers have shifted their tactics to exploit trust itself. Malware today is no longer defined by static signatures or known indicators. It is adaptive, AI-generated, and designed to look entirely legitimate until it is too late.

Traditional tools that focus on what code looks like or where it came from are working from the wrong starting point. They make security decisions based on appearance and origin, and sophisticated threats are built specifically to pass those checks.

CodeHunter is proud to be named a winner of the 2026 Global InfoSec Award for Next-Gen Behavioral Malware Analysisat the RSAC 2026 Conference. This recognition reflects a fundamental shift in how code must be evaluated and controlled before it is authorized to run.

Verifying Intent with Zero Trust for Code

Zero Trust for Code starts from a different premise than traditional security tools. Instead of assuming software is safe because of its reputation, its origin, or how it looks, the framework holds that every artifact is untrusted by default. Trust is not conferred. It is earned through behavioral verification.

CodeHunter’s behavioral intent analysis deconstructs any software artifact, whether a binary, script, container, package, or AI-generated file, to surface its full behavioral capability. Every system interaction, network behavior, privilege operation, and persistence mechanism is identified before the execution decision is made. The result is a deterministic verdict: Allow, Block, Contain, or Escalate. Backed by forensic evidence. Auditable. Tied to explicit policy. This is what makes Zero Trust for Code actionable rather than theoretical.

Why Intent Is the Only Reliable Standard

Not all threats behave the same way, and that variation is intentional. Advanced threats are built to be stealthy, to blend into normal activity, to delay execution until trigger conditions are met, and to leverage legitimate system processes so their behavior does not stand out. Appearance-based controls cannot reliably catch threats designed to look legitimate. Origin-based controls cannot catch threats delivered through compromised but trusted channels.

The only standard that holds across all of these scenarios is behavioral intent: what is this code actually designed to do? When the analysis is pre-execution and the verdict is deterministic, there is no window for a sophisticated threat to exploit. The code is evaluated before it runs, and the decision is made by policy rather than by default.

Proactive Security Across the Full Lifecycle

Pre-execution behavioral intent analysis is not a single point control. It applies consistently across internal development artifacts,third-party dependencies, endpoint executables, and cloud workloads. The same behavioral standard governs code in the CI/CD pipeline and code on a remote laptop.

That consistency closes the gaps between development and production that attackers have learned to exploit. It reduces manual triage because verdicts are deterministic rather than probabilistic, and it transforms behavioral analysis from something that happens after an alert into something that prevents the alert from being generated in the first place.

Winning this award reinforces what CodeHunter customers already know. The future of security depends on asking a better question: not has this been seen before, but what can this software do? When you understand intent, Zero Trust for Code becomes actionable. When Zero Trust extends to code execution, prevention becomes possible. Read the full press release here.

zero trust for code and pre execution defense model

Moving Behavioral Analysis Upstream: Pre-Execution Defense in CI/CD and Beyond 

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The way software enters the enterprise has fundamentally changed. Organizations are no longer installing a handful of vetted applications. They are moving thousands of executable artifacts through CI/CD pipelines at machine speed, and when code volume increases this rapidly, the traditional window security vetting collapses. Waiting on sandbox detonation or a signature match becomes a bottleneck that most teams eventually bypass just to keep pace with production. That bypass is where the risk lives.

The Problem with Reactive Vetting

Most supply chain security focuses on who signed the code or what the code looks like compared to known threats. In a modern environment where AI-generated code and mutating artifacts are routine, those indicators are easily spoofed or bypassed. A signed binary from a compromised vendor is still a signed binary. An AI-generated payload carrying no prior signature clears every pattern-matching check in the stack.

If analysis only happens at the endpoint, security is already playing catch-up. By the time an artifact executes, the risk is live, and moving analysis upstream, into the development and delivery pipeline before code reaches a production environment, is the only approach that changes the sequence from reactive to preventive.

Our  recent announcement on software supply chain security  reflects exactly that logic. It is not a pivot in our technology. It is the logical extension of the behavioral intent analysis CodeHunter has always practiced, applied to the point in the software lifecycle where intervention still matters.

Deterministic Decisions, Not Guesses

CodeHunter has never relied on signature matching. Behavioral intent analysis deconstructs what an artifact is programmatically capable of doing, producing a Behavioral Intent Profile that captures the full range of behaviors the artifact can exhibit.

  • Does a signed binary attempt privilege escalation have no business performing?
  • Does an internally developed tool initiate unexpected network connections?
  • Does an AI-generated package exhibit persistence mechanisms that were never part of its specification?

The results are deterministic, and every verdict is explainable and auditable. Security leaders know exactly why an artifact was blocked or contained, not just that an algorithm assigned it a high-risk score. In an era of black-box security tools, that transparency is not a nice-to-have. It is a requirement for any execution decision that has to hold up to compliance review

Closing the Loop: From Pipeline to Production

Moving analysis upstream is essential for prevention, but a complete strategy also requires consistency across the entire software estate. The same behavioral engine that evaluates artifacts in the CI/CD pipeline is also used to resolve noise in your existing security stack. When SentinelOne or Microsoft Defender triggers an alert on a suspicious or unknown file, CodeHunter automatically pulls that artifact for deep behavioral intent analysis. The verdict is issued against the same Behavioral Intent Profile standard, whether the file was found in a developer’s build or on a remote endpoint.

That consistency produces three practical outcomes. First, operational consistency: a single authoritative verdict regardless of where the artifact was discovered, eliminating the scenario where pipeline security and endpoint security are working from different assumptions. Second, response speed: automated analysis of EDR alerts produces a deterministic verdict in minutes, removing the analyst triage step that slows incident response. Third, unified visibility: when a threat found by your EDR matches behavioral capabilities seen earlier in your CI/CD pipeline, you see it, and the connection between upstream and downstream is visible and documented.

Pre-Execution Trust Across the Full Lifecycle

By integrating behavioral intent analysis into CI/CD workflows while simultaneously supporting SOC teams with automated artifact analysis, CodeHunter enables organizations to enforce execution policy at every stage of the software lifecycle. Every artifact is untrusted by default. Trust is earned through behavioral verification. That principle applies in the pipeline before deployment, at the endpoint before execution, and everywhere in between.

Find out how CodeHunter integrates behavioral intent analysis directly into your DevSecOps workflow.

CodeHunter logo representing Zero Trust for Code and pre-execution control over what software is allowed to run

2025 Cybersecurity Predictions: The Year Zero Trust for Code Becomes Unavoidable

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2024 was a defining year for cybersecurity, with significant breaches, meaningful defense wins, and a threat landscape that continued shifting faster than most organizations could track. The MOVEit vulnerability demonstrated how a single flaw in widely used software can cascade across hundreds of organizations simultaneously. Coordinated government action disrupted several prominent criminal operations. The lessons from both sides of that ledger are shaping what 2026 demands from security teams. 

Like the threats it defends against, this industry is always moving. Here is what the current trajectory points toward. 

AI-Generated Code Risk Becomes a Board-Level Governance Question 

Generative AI has changed the threat landscape in two directions at once. For attackers, it has lowered the barrier to producing functional malicious code to nearly nothing. AI-generated variants that carry no prior signature arrive continuously, and they evade detection tools built around recognition. For defenders, AI assists with pattern recognition, accelerates analysis, and helps security teams process volumes of data that would otherwise overwhelm them. 

The governance gap sits between those two realities. AI coding tools generate executable artifacts that enter development pipelines faster than any manual review process can evaluate them. In 2026, that gap reaches the board level. Executives and compliance teams will begin asking for documented, policy-backed answers about what AI-generated artifacts are running in their environments and what standard governs their authorization. 

Behavioral Intent Analysis Replaces Signature-Dependent Approaches 

Signature-based detection identifies what it has already seen. AI-generated code, novel variants, and purpose-built payloads are specifically designed to be things that have not been seen before, and that structural mismatch between a recognition-based defense and a novelty-based offense is not going to resolve in favor of signatures. 

Behavioral intent analysis asks a different question: what is this artifact designed to do? It does not require prior knowledge of the threat, deconstructs the artifact itself to surface behavioral capability before execution is authorized, and produces a deterministic verdict rather than a probability score. The combination of pre-execution behavioral analysis and automated artifact deconstruction is what allows SOC teams to move quickly between identification, containment, and response without being overwhelmed by volume. 

Agentic Workflows Emerge as a Distinct Security Category 

Agentic workflows, AI systems that autonomously generate and execute code without a human authorization step, are creating a supply chain risk category that existing controls were not designed to govern. An agentic pipeline that retrieves an external package and executes it. An AI coding tool that generates a script and runs it immediately. A development pipeline where AI-generated contributions are merged and deployed without a behavioral verification gate. Each of these scenarios introduces executable artifacts into production environments through trusted internal channels, without any behavioral verification step, and agentic supply chain risk will surface as a distinct security category in 2025. 

Pre-Execution Enforcement Becomes the Practical Answer to Detection Overload 

SOC teams are not going to scale their way out of the volume problem. Adding analysts does not keep pace with the rate at which AI generates new, signature-free threats. Moving enforcement upstream, to the execution layer, is the answer that scales. When artifacts are evaluated and a verdict issued before they run, fewer alerts are generated downstream, and the SOC receives genuine escalations rather than noise from code that should never have been authorized to execute in the first place. 

Automation empowers security teams to operate at the speed of modern threats demand. Zero Trust for Code is the framework that makes that automation purposeful. Find out how CodeHunter applies to your existing security stack.

Interconnected circuitry forming a padlock, representing secure control over software execution and the verification of code behavior before it runs

Software Supply Chain Security: Why Pre-Execution Defense Is the Missing Layer 

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Software supply chain attacks are on the rise, and the reason is straightforward. A successful attack on any single link in the chain can spell disaster downstream. As software becomes more complex and interconnected, attackers have more entry points, more trusted channels to exploit, and more cover for the code they introduce.

The deeper problem is structural. Most cybersecurity solutions available today are built to detect known threats. By the time a security team identifies a new attack, the effects have already traveled down the chain. Reactive defenses that wait for something to look wrong are not a supply chain security strategy. They are a cleanup plan.

Defending software supply chains requires answering a question that existing tools were never designed to ask: what will this code do when it executes?

Trusted Sources Are Not Trusted Behavior

Threat actors approach supply chain attacks by undermining code signing, forging their way into a software supply chain under the guise of a known and trusted author. The fundamental problem is that organizations extend trust based on where code came from rather than what it will do.

CodeHunter operates on a different principle: every artifact is untrusted by default, regardless of its source. Where a manual check or preconfigured rule might wave through code from a trusted vendor, CodeHunter’s pre-execution behavioral analysis evaluates what that code is capable of doing before it is allowed to run, every time, without exception.

Software updates present the same risk. A threat actor who compromises a vendor’s update pipeline delivers malicious behavioral capability through a channel the target organization has explicitly trusted. Combing through every update manually would be prohibitively slow and expensive. CodeHunter deconstructs the artifact’s behavior automatically, issuing a deterministic verdict in a fractionof the time it would take an analyst to complete the same review.

Open-Source Code Is Not an Exception

Compromised open-source code is one of the most underestimated supply chain risks. The Linux backdoor discovered in the XZ Utils compression library is a clear example: a single contributor embedded a backdoor into widely trusted code that had been in use for years. Researchers caught it before it reached production systems, but that outcome was fortunate rather than systematic.

The sheer scope of open-source dependencies makes manual review impractical at scale. CodeHunter can be configured to automatically scan entire directories and networks, locally or in the cloud, to identify behavioral capabilities that should not be there. The question is never whether the code looks familiar. The question is what the code will do.

What Humans Miss, Behavioral Intent Analysis Catches

Valid credentials were the preferred initial access technique of cybercriminals last year, with a 71% increase in attacks leveraging stolen account access. Information stealers that harvest those credentials are often delivered through code that looks entirely legitimate. CodeHunter’s pre-execution behavioral analysis evaluates what code is capable of doing at the artifact level, not the filename level. Suspicious behavioral capability is surfaced regardless of how the artifact is packaged, named, or signed.

Unknown Threats Have Behavioral Signatures Too

Not every supply chain threat arrives with a known fingerprint. Behavioral intent analysis does not depend on prior knowledge of the threat. It deconstructs the artifact to surface what it is programmatically designed to do, and a trojan that has never been catalogued still has behavioral characteristics that are present in the artifact before it ever runs.

The Cost of Letting Threats Sit Undetected

The SolarWinds attack remains the clearest illustration of what delayed detection costs. Eighteen thousand customers unknowingly downloaded a malicious update, and the intrusion went undetected long enough to cause an estimated $90 million in insured losses. IBM put the average cost to remediate a software supply chain compromise at $4.63 million in 2023. The earlier a malicious artifact is identified, the less damage it causes, and CodeHunter is designed to catch artifacts at the threshold, before they execute, not after the damage is done.

Empower Your Software Supply Chain Security

CodeHunter’s combination of scalability, automation, and pre-execution behavioral analysis makes it the practical defense for organizations that cannot afford to let signed, trusted-looking code run unchecked. Speak with our team to learn more about how CodeHunter applies Zero Trust for Code to software supply chain security.

digital security imagery representing software execution activity and the importance of verifying and controlling code behavior before it is allowed to run

Defense-in-Depth in 2026: Adding the Execution Control Plane Above EDR

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The probability of encountering advanced code-based threats, including zero-day exploits, multi-stage payloads, and purpose-built attacks, continues to rise. Threat actors persist in finding new ways into secured corporate networks, and services that offer ready-made attack infrastructure have made sophisticated campaigns accessible to actors with limited technical backgrounds of their own. 

For organizations to stay ahead of a breach, a multi-layered security posture is not optional. It is the baseline. Defense-in-depth, the practice of combining multiple layers of controls that compensate for each other’s limitations, remains the right strategic framework. The question in 2026 is not whether to practice defense-in-depth, but whether the layers you have actually cover the execution surface where modern attacks land. Most do not. The missing layer is the execution control plane. 

Where the Existing Layers Perform Well 

Before addressing the gap, it is worth being precise about what existing defense-in-depth layers do well, because Zero Trust for Code complements the stack rather than replacing it. 

Cybersecurity awareness training reduces the human error that attackers exploit through social engineering and phishing. Network segmentation limits an attacker’s ability to move laterally after gaining initial access. Regular patching reduces the window of exposure on known vulnerabilities. Multi-factor authentication adds meaningful friction to credential-based attacks. EDR provides visibility into behavior at the endpoint and detection of anomalies after execution begins. 

Each of these layers is valuable. Each one also operates either before an artifact enters the environment or after it has already executed. None of them systematically answers the question that should gate execution: what will this code do when it runs? 

The Gap in the Stack 

Traditional security measures do a reasonable job identifying known threats. They are not designed to evaluate complex, novel, or AI-generated artifacts that carry no prior signature, and the gap is structural. Existing layers cannot catch what they do not know to look for. 

Behavioral intent analysis addresses this gap directly. Rather than comparing an artifact against a catalog of known threats, it deconstructs the artifact to surface what it is programmatically capable of doing. That capability profile is what should drive the execution decision, not the artifact’s resemblance to something previously observed. The analysis sits above EDR, authorizing what is allowed to execute before downstream detection tools ever see it. 

How CodeHunter Strengthens Defense-in-Depth 

CodeHunter’s patented behavioral intent analysis automates the artifact deconstruction process that previously required months of expert work. Operating at binary code level, the platform evaluates any executable artifact, whether a binary, script, container, package, or AI-generated file, and produces a deterministic verdict: Allow, Block, Contain, or Escalate. That verdict arrives before execution, and the forensic evidence behind it is auditable and mapped to MITRE ATT&CK. 

Applied across the defense-in-depth stack, this means every artifact entering the environment is evaluated for behavioral capability before execution is authorized, closing the window that attackers have learned to exploit between delivery and detection. SOC teams receive fewer alerts from code that should never have been permitted to run. Compliance teams have a documented, policy-backed record of every execution decision. DevSecOps teams catch risky artifacts in the CI/CD pipeline before they reach production, replacing post-incident response with pre-execution enforcement. 

The Execution Control Plane as the Missing Layer 

Zero Trust for Code is the framework that makes defense-in-depth complete. It does not replace the layers already in your stack. It fills the gap those layers leave open, which is the execution authorization decision that has historically been made by assumption rather than by policy. 

Every artifact is untrusted by default. Trust is earned through behavioral verification. The verdict is deterministic. The evidence is forensic. That is what it means to govern the execution layer rather than hope for the best about it. Speak with the CodeHunter team to learn how pre-execution behavioral intent analysis integrates into your existing defense-in-depth strategy.