The rise of agentic AI represents a tectonic shift in the world of software development, introducing tools capable not just of faster, more efficient production but also of autonomous reasoning and actions that operate outside the scope of human intervention. In short, artificial intelligence agents have completely changed the game.
Enterprises can’t hope to make the best, safest use of these powerful AI tools if they’re still playing by the old rules of software development, or with the same referees. Although agentic AI promises unprecedented efficiency in development, it amplifies common security vulnerabilities and introduces new, AI-specific weaknesses, significantly increasing the risks of technical debt, especially when paired with the power and far-reaching connectivity of Model Context Protocol (MCP).
To avoid being overrun by agentic AI’s power, organizations need to take a new approach to governance that works at the speed of AI agents, incorporating real-time monitoring, automated guardrails and dynamic policy frameworks that can keep autonomous innovation from outpacing our ability to manage the lifecycle.
In fact, agentic AI changes the game so much that it may be time to put old ideas about the software development lifecycle (SDLC) to bed and adopt an agentic development lifecycle (ADLC) model that accounts for agentic AI’s particular qualities. As AI transformation company EPAM, a proponent of ADLC, has pointed out, SDLCs for decades were based on the idea that software’s behavior was locked down once it entered production; the software would do what it was programmed to do. Agentic AI blows apart those assumptions, since the whole point of AI agents is that they are going to adapt, learn, make decisions and act in ways software engineers may not have expected and certainly can’t control.
ADLC is intended to address a software environment in which large language models (LLMs) are at the center of product development. It’s part of an approach that can enable organizations to implement governance practices that are up to the task of addressing the risks of agentic AI.
AI Agents Magnify Security Weaknesses
Agentic AI, which typically makes use of large language models such as Claude, Gemini, ChatGPT and a wide range of others, brings a raft of vulnerabilities to the table because of its power to draw on multiple sources and make independent decisions. Adding vulnerabilities like prompt injection, deepfakes, training data poisoning, supply chain vulnerabilities and the unintentional disclosure of sensitive data is common among AI agents when performing tasks such as writing code. LLMs bring enough vulnerabilities to the table that they have their own OWASP Top Ten list of risks.
The ubiquitous use of LLMs and other AI tools by employees adds to the attack surface and organizational risk profile, as does, alarmingly, the use of agentic workflows by developers who have low proficiency in security. If the software code that agentic AI is using isn’t secure, then the organization’s foundation rests on quicksand.
Meanwhile, the vulnerabilities associated with agentic AI are being amplified by another potent innovation: MCP. The Model Context Protocol (MCP) gives LLMs and, therefore, AI agents a standardized means of communicating with data and applications throughout the enterprise by connecting with databases, APIs, business systems and developer environments. Before MCP was introduced in late 2024, agentic AI typically required custom integration to interact with business applications, which could be time-consuming to build. MCP makes that connectivity easy, increasing the speed and reach of AI agents in gathering information. MCP allows an AI agent to access data from almost anything, from a Teams meeting to a database to a repository of software code.
MCP undoubtedly increases the benefits of agentic AI, particularly in terms of productivity and efficiency, but it also expands the risks and the attack surface. As the threat landscape evolves, attackers will look to take advantage of agentic AI’s autonomy, bidirectional communications and analytical power to use against organizations. MCP is becoming so popular that employees in some places are installing MCP servers without telling their cybersecurity teams, creating a whole new level of shadow AI.
Why Fast-moving Governance is Essential
Organizations have a number of options available for mitigating the risks of agentic AI. Among them are practical steps such as minimizing their access to sensitive and/or untrusted data and limiting their ability to communicate externally. Sandboxing or running AI agents and MCP servers inside containers are other potential steps. Applying zero-trust principles, such as authorizing and authenticating tools, could also help along with a comprehensive logging of tools used, data accessed and commands executed.
And because AI agents act autonomously, implementing the same kind of behavior monitoring used for human users could help flag anomalous or dangerous behavior.
But perhaps more than any other defensive step, organizations need agile, robust AI software governance. This would involve several of those practical steps, such as relentless real-time monitoring, along with automated guardrails for AI use, and dynamic policy frameworks that can evolve with the use of agentic AI to ensure that autonomous innovation doesn’t outpace our ability to control the emerging ADLC. Organizations need a high level of observability and traceability of MCPs, for example, as well as up-to-date information of which agentic AI tools are in use, who is using them and how they are being employed.
It requires a whole-of-enterprise approach, encompassing both the automated tools to monitor AI use and the human element. Developers, for example, need cybersecurity upskilling to ensure they can review and correct AI output. Security leaders and development managers can restructure their programs to promote continuous observability of AI and MCP use, while enabling developers by providing relevant, hands-on adaptive learning.
Traditional oversight is no longer sufficient; we are moving from “human-in-the-loop” to “human-on-the-loop.” To harness this power safely, enterprise organizations must implement reliable AI software governance that matches the velocity of the agents themselves, not to mention the specific security needs of the company.
