An unfamiliar program runs on a developer’s computer, accesses stored credentials, and connects to an external server. An endpoint detection and response (EDR) system raises an alert, leaving the security team with two immediate questions: Is the code malicious, and what has it already done?
Generative AI has made this situation more complicated. Attackers can use AI tools to create and modify malicious code, while developers rely on the same technology to build applications, automate tasks, and write scripts. As a result, security teams must evaluate unfamiliar code from different sources without assuming that new or unusual code is automatically dangerous.
The real concern begins when that code has access to sensitive credentials, production systems, or deployment tools. Waiting for suspicious activity to appear may mean investigating a potential compromise after the damage has already occurred. Organizations therefore need to consider how they can assess code, enforce security policies, and limit access before execution, while continuing to monitor activity once a program starts running.
The Growing Challenge of Investigating Unfamiliar Code
Security alert volume is often treated as a staffing issue, but it also creates a decision-making challenge. Every alert that cannot be resolved automatically requires someone to investigate the activity, assess the potential risk, and decide whether the program should be blocked, contained, or allowed to continue.
The level of risk depends heavily on the environment. A suspicious script running inside an isolated test environment may require a relatively straightforward review. The same script operating in a build system with access to cloud credentials, software-signing keys, or production deployment permissions presents a much more serious concern.
AI adds another layer of complexity by making it easier to generate variations of existing malicious code. These variations may look different from previously identified threats, making detection methods based primarily on known files or signatures less effective.
However, unfamiliar code does not necessarily represent a new attack technique. Different programs can perform the same harmful actions, such as stealing credentials, communicating with malicious servers, or attempting to maintain access to a system. This is why behavioral monitoring remains an important part of modern security strategies.
Ultimately, the workload facing security teams depends partly on how many alerts require manual investigation and how effectively existing controls can distinguish legitimate activity from genuine threats.
Risk Scores Must Lead to Clear Security Decisions
Risk scoring can help security teams prioritize threats, particularly when the available evidence is incomplete. A program’s behavior, origin, permissions, and operating environment can all provide useful signals when assessing whether it should be trusted.
However, a risk score alone does not provide an effective security policy. Organizations need clear rules that explain what happens when a program crosses a particular risk threshold. Depending on the circumstances, the appropriate response might involve blocking execution, restricting access to sensitive resources, isolating the program, or sending the activity for additional review.
Context is especially important. A script that presents a limited risk in a development environment may be unacceptable on a production server. Similarly, unfamiliar code running under a standard user account creates a different level of exposure from code operating with administrator privileges.
Security decisions should also be explainable. Teams need to understand which evidence, policy, and environmental conditions led to a program being allowed or blocked. Consistent rules make it easier to investigate incidents, review exceptions, and improve security controls over time.
This approach also helps prevent legitimate development work from being disrupted unnecessarily. When policies account for differences in permissions, environments, and business requirements, organizations can strengthen security without treating every unfamiliar program as a threat.
Why Security Investigations Become More Complicated After Execution
Once suspicious code has executed, security teams must determine not only what the program was designed to do but also what it actually did. That investigation may involve checking whether the program accessed credentials, modified files, established persistence, contacted attacker-controlled infrastructure, or attempted to move to other systems.
Even when investigators find no evidence of a successful compromise, establishing what happened can consume considerable time. They may need to examine system logs, network connections, process activity, and access records before deciding whether further containment is necessary.
Development environments can introduce additional risks because code often runs with the permissions of the account that launches it. For example, a package installation script may execute automatically during installation, while an AI-generated automation script might run under a privileged service account.
If these accounts can access deployment systems, sensitive environment variables, or cloud credentials, a seemingly routine development task can create a significant security exposure.
Detection and containment tools remain essential, but they cannot always prevent the initial access or determine immediately how much damage occurred before an alert was triggered. Checking code earlier in the workflow can help identify policy violations and reduce unnecessary exposure before a program reaches a sensitive environment.
Building Security Into the Workflow Before Code Runs
Organizations can strengthen their defenses by identifying the points where unfamiliar code enters their environments. These may include downloaded scripts, AI coding agents, third-party packages, developer workstations, and continuous integration and delivery (CI/CD) pipelines.
The first step is to understand which accounts execute this code and what those accounts can access. If a task requires only limited permissions, there is little reason for the process to have unrestricted access to production systems or sensitive credentials.
Before allowing unfamiliar code to run in a privileged or production environment, teams can review its source, inspect its contents, and test it in an isolated environment where appropriate. These checks can reveal suspicious operations or violations of established security policies.
Application controls can also restrict which programs are permitted to execute. Combined with the principle of least privilege, these controls can limit the potential impact of code that passes an initial review but later behaves unexpectedly.
However, pre-execution checks have limitations. Reviewing source code or testing a program in isolation cannot guarantee that every possible behavior will be identified. Some threats emerge only under particular conditions, when a program interacts with other systems or receives specific inputs.
For that reason, organizations should combine earlier security checks with runtime monitoring and established incident response procedures. EDR systems can continue to identify suspicious behavior during execution, while pre-execution controls help prevent known policy violations and provide additional context for security decisions.
Teams should also measure whether these measures deliver practical improvements. Useful indicators include investigation time, the number of incidents requiring manual review, false-positive rates, blocked execution attempts, and delays introduced into legitimate development workflows.
The goal is not to prevent developers from using AI or to block every unfamiliar program. It is to make sure that code receives an appropriate level of scrutiny before it gains access to sensitive systems.
A More Balanced Approach to AI-Era Code Security
AI-generated code is becoming part of everyday software development, and malicious actors can use the same capabilities to create and modify harmful programs. This makes it increasingly important for organizations to evaluate code based on its behavior, permissions, source, and operating context rather than relying on familiarity alone.
Waiting until suspicious activity appears can leave security teams with a larger investigation and fewer opportunities to prevent exposure. Pre-execution checks, clear enforcement policies, application controls, and restricted permissions can reduce that risk, while runtime monitoring remains necessary to detect threats that emerge during execution.
A balanced strategy combines prevention with detection and response. By checking unfamiliar code earlier, limiting what it can access, and maintaining visibility into its behavior, organizations can reduce avoidable security incidents without unnecessarily slowing legitimate development.
As AI-assisted development continues to expand, the ability to make consistent, context-aware security decisions before execution will become an increasingly important part of protecting modern software environments.
