Compare the Top Code Security Tools in India as of September 2026

What are Code Security Tools in India?

Code security tools help developers and security teams identify, analyze, and fix vulnerabilities in source code to prevent security breaches and reduce risk. They automatically scan codebases for issues such as insecure patterns, misconfigurations, and known vulnerabilities using both static and dynamic analysis techniques. These tools often integrate with development environments, CI/CD pipelines, and code repositories to provide real-time feedback and continuous security checks. Many code security solutions also include reporting, remediation guidance, and compliance support to enforce security policies. By improving code security early in the development lifecycle, these tools help teams deliver more secure, reliable software. Compare and read user reviews of the best Code Security tools in India currently available using the table below. This list is updated regularly.

  • 1
    Backslash Security
    The software development lifecycle has fundamentally changed. Developers across engineering organizations are using AI coding tools — GitHub Copilot, Cursor, Windsurf, Claude Code, Gemini CLI — at scale. The security controls built for traditional development were not designed for this environment. Backslash Security addresses this gap directly. The platform gives security teams visibility into AI coding tool usage, the code being generated, MCP server connections made by AI agents, and the risk introduced before it reaches production. Core capabilities: AI coding tool inventory and policy enforcement MCP server visibility and access control Vibe coding security — risk detection in AI-generated code Continuous monitoring without disrupting engineering workflows Purpose-built for AI-native development — not a legacy scanner repositioned for a new market. For security leaders governing an environment they didn't design, Backslash provides the visibility and control you need.
  • 2
    DryRun Security

    DryRun Security

    DryRun Security

    DryRun Security brings AI Native SAST and Agentic Code Security to your code, so application security and dev teams can stop triaging noise and start fixing real risk. Our Contextual Security Analysis (CSA) engine reasons about code intent, exploitability, and impact to deliver high-signal findings that pattern-matching scanners miss. Use the Code Review Agent for PR comments and checks within moments of a push. Enforce guardrails with Natural Language Code Policies, written in plain English and executed by the Custom Policy Agent on every PR. Run DeepScan Agent for an on-demand full-repo assessment in about an hour, and use Code Insights Agent to see trends and risk across repos.
  • 3
    Asterisk

    Asterisk

    Asterisk

    Asterisk is an AI-driven platform that automates the detection, verification, and patching of security vulnerabilities within codebases, effectively emulating the approach of a human security engineer. It excels in identifying complex business logic errors through context-aware scanning and provides comprehensive reports with near-zero false positives. Key features include automated patch generation, continuous real-time monitoring, and extensive support for major programming languages and frameworks. Asterisk's process involves indexing the codebase to create accurate call stack and code graph mappings, enabling precise vulnerability detection. The platform has demonstrated its efficacy by autonomously discovering vulnerabilities in systems. Founded by a team of seasoned security researchers and competitive CTF players, Asterisk is committed to leveraging AI to streamline code security audits and enhance vulnerability discovery.
  • 4
    Codex Security
    Codex Security is an AI-powered application security agent developed by OpenAI to help teams detect and fix vulnerabilities in software systems. The tool analyzes code repositories to understand the structure, architecture, and potential risk areas within a project. Using this context, it identifies complex security issues that traditional scanning tools might overlook. Codex Security prioritizes vulnerabilities based on their real-world impact, helping security teams focus on the most critical threats. The system also validates findings through sandboxed testing environments to reduce false positives and improve accuracy. Once vulnerabilities are confirmed, it proposes patches and remediation steps that align with the system’s existing behavior. By combining AI reasoning with automated validation, Codex Security helps development teams ship more secure code faster.
  • 5
    depthfirst

    depthfirst

    depthfirst

    depthfirst is an AI-native application security platform designed to help organizations detect, prioritize, and fix software vulnerabilities by deeply understanding their code, infrastructure, and business logic as a unified system. depthfirst, built around its core “General Security Intelligence,” analyzes entire repositories and environments to map how systems actually function, enabling it to uncover complex, real-world vulnerabilities that traditional scanners often miss. It evaluates full attack paths, permissions, and data flows to determine whether an issue is truly exploitable, significantly reducing false positives and allowing teams to focus only on meaningful risks. depthfirst operates across multiple layers of the stack, including source code, dependencies, secrets, containers, and running applications, providing continuous security coverage from development through production.
  • 6
    OpenAI Daybreak
    OpenAI Daybreak is frontier AI for cyber defenders and OpenAI’s vision for changing the way software is built and defended. Daybreak means seeing risk earlier, acting sooner, and helping make software resilient by design, starting from the premise that the next era of cyber defense should be built into software from the beginning. It is not only about finding and patching vulnerabilities, but about helping systems become resilient to them by design. Daybreak brings AI into modern cyber defense by helping defenders reason across codebases, identify subtle vulnerabilities, validate fixes, analyze unfamiliar systems, and move from discovery to remediation faster. Because those same capabilities can be misused, Daybreak pairs expanded defensive capability with trust, verification, proportional safeguards, and accountability. It combines the intelligence of OpenAI models, the extensibility of Codex as an agentic harness, and security partners across the security flywheel.
  • 7
    Google AI Threat Defense
    Google AI Threat Defense is an AI-powered cybersecurity platform designed to help organizations proactively predict, prioritize, and remediate threats at machine speed. Combining the reasoning capabilities of Gemini, contextual risk analysis from Wiz, automated code remediation through Gemini and CodeMender, and frontline threat intelligence from Mandiant, the platform enables security teams to continuously identify exposures, validate risks, accelerate remediation, and monitor environments for emerging threats. Built around a four-step framework of Prepare, Scan, Remediate, and Monitor, Google AI Threat Defense helps organizations strengthen security across multicloud, AI, SaaS, code, and hybrid environments while reducing response times and improving operational resilience against modern AI-driven attacks.
  • 8
    Pi

    Pi

    Pi Security

    Pi is an agentic product security platform that builds institutional security memory so organizations can find, fix, and prevent recurring vulnerabilities without slowing development. It continuously ingests codebases, past incidents, pentest reports, tickets, and other security history into a living inventory, giving the system context about how the organization builds and secures software. When a vulnerability is found, Pi traces it to its architectural root cause, searches for variants across the codebase, and helps close the entire class of issue rather than treating each finding independently. Remediation is generated in the context of the organization’s languages, architecture, and conventions, then delivered directly into developer workflows. What the system learns becomes prevention guardrails that can be applied in IDEs and pull requests during design and coding, blocking known insecure patterns before they reach production.
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