Would you rather have determined that you are in fact secure, or are you willing to accept that you are "probably" doing things securely? This might seem like a silly question on the surface, after all, audits don't work on probability; they work on documented facts. We are in an era of rapidly advancing artificial intelligence-driven applications across our organizations, covering a wide variety of use cases. We love LLMs at GitGuardian and think there are a lot of advantages to appropriately applying AI, but like any other technology, if we misunderstand and misuse AI, we could be opening doors to new dangers.As we further adopt these tools and apply AI in novel ways to our workflows across all departments, it is likely a good idea to take a step back and think through how these systems work, which boils down to most likely getting a correct answer versus repeatedly getting the same expected results based on the same specific inputs.Let's take a closer look at how probabilistic systems benefit security and how, if we're not careful, they can potentially lead us astray and leave us vulnerable.
Deterministic vs. Probabilistic Systems
Deterministic systems are the bedrock of security because they leave no room for interpretation. Given the same inputs, they will always produce the same outputs, which means their behavior can be modeled, tested, and formally verified. Cryptographic hash functions, for example, don’t “probably” match; either the hash aligns or it doesn’t. The same goes for network routing algorithms and identity protocols like FIDO2, which provide definitive guarantees that an identity is valid and a transaction is authentic. This predictability allows security teams to enforce strict controls and build trust anchors that do not drift over time.
Probabilistic systems, on the other hand, introduce uncertainty by design. Large language models, Bayesian inference engines, and even quantum measurements operate within distributions rather than fixed outcomes. While this makes them powerful tools for pattern recognition, anomaly detection, and contextual reasoning, it also means they cannot offer the same binary assurances. Instead of proofs, probabilistic systems provide confidence scores, which may be enough for advisory layers like threat detection or risk prioritization but not for trust-critical enforcement. In short, probability is excellent for finding signals, but dangerous when used to define the rules of access or authorization.
Two Differing Use Cases: Predictive vs Generative AI
Predictive AI and generative AI share a common foundation in machine learning, but they serve fundamentally different purposes. Predictive AI focuses on forecasting outcomes based on historical data. It powers tools like fraud detection models, recommendation engines, and risk scoring systems. From a security standpoint, predictive AI is relatively bounded: it operates within predefined input-output relationships and can be evaluated against measurable accuracy. This makes it easier to validate, audit, and integrate into existing security workflows because its behavior is more deterministic, even if it is statistically driven.
Generative AI, on the other hand, creates text, video, images, and more. Tools like GitHub Copilot and large language models don’t simply predict a label; they generate new text, code, or artifacts based on probabilistic reasoning. This creative flexibility is what makes them powerful, but also introduces unique risks. A developer using Copilot may receive a code suggestion that looks technically correct but tells them to do something fundamentally unsafe, like using outdated libraries or even to hardcode secrets. These models optimize for plausibility rather than correctness; there’s no guarantee that what they generate aligns with secure coding practices. Without proper guardrails, developers risk introducing insecure code into production faster than ever before, scaling not just productivity but potential vulnerabilities.
This is where security must become proactive. Developers need deterministic guardrails around generative tools like Copilot; automated secret scanning, dependency checks, and policy enforcement that validate every AI-assisted change before it merges. GitGuardian plays a critical role in this workflow by continuously monitoring for exposed credentials and integrating security directly into the development process. By pairing probabilistic generation with deterministic enforcement, teams can safely leverage AI’s speed without sacrificing control, turning Copilot from a potential liability into a secure accelerator for software delivery.
Agentic AI Brings New Security Risks
Agentic AI is a class of AI systems that don’t just generate or predict; they autonomously take actions, chain tasks, and interact with external systems or other agents to achieve goals. This shift from passive output to active execution expands the attack surface, making deterministic identity enforcement and strict authorization controls absolutely critical for security.
These autonomous agents can chain API calls, interact with external systems, and even coordinate with other agents to achieve complex goals. When their decision-making is driven by probabilistic reasoning, it becomes possible for them to bypass deterministic controls like traditional authorization checks.
If an AI agent “thinks” it’s 95% certain it should never give all the user data to a requesting API call, that five percent uncertainty is enough to turn a clever prompt or manipulated response into a security breach. Unlike a deterministic system, which fails safely, a probabilistic agent may act on an assumption, and in security, assumptions are where attackers live.
Good Security Leveraging Probabilistic Systems
From a security standpoint, there are places where probability belongs, and places where it absolutely does not. Identity authentication, transaction authorization, cryptographic key validation, and agent permissions must be rooted in deterministic validation, not statistical confidence. Generative AI, while powerful, can easily mislead developers, suggesting insecure code, leaking secrets through logs, or introducing unsafe patterns without clear visibility.
Even well-structured Retrieval-Augmented Generation (RAG) systems have a fundamental limitation: you can’t “tune” them for security beyond scrutinizing all input and output, leaving room for mistakes that attackers can exploit. This is why GitGuardian treats probabilistic intelligence as a supplement rather than a trust anchor, reinforcing every critical security decision with deterministic, provable checks.
GitGuardian believes that predictive AI can be used safely when paired with deterministic enforcement. The platform applies LLMs and contextual analysis to dramatically improve detection accuracy; its False Positive Remover has cut false positives by up to 80%, but it does not stop at “probably a secret.” With GitGuardian, secret candidate matches are validated using a combination of regular expressions and heuristics based on contextual information to eliminate uncertainty. This combination allows developers to benefit from the speed and context of AI while maintaining the rigor of deterministic security.
This layered approach matters even more in environments where AI is increasingly embedded into development workflows. Generative AI tools, such as code assistants like GitHub Copilot, can accelerate productivity, but they can also normalize insecure patterns, accidentally expose secrets, or add complexity to already-overloaded pipelines. Without deterministic guardrails, these risks compound and silently erode the foundation of an organization’s security posture. GitGuardian provides these guardrails via our VS Code extension and pre-commit hooks leveraging ggshield, giving developers automated, context-rich detection backed by deterministic validation, so they can innovate faster without sacrificing safety.
Furthermore, GitGuardian’s design accounts for the growing role of non-human identities (NHIs), including agentic AI in enterprise ecosystems. As these entities interact with APIs, infrastructure, and each other, deterministic enforcement ensures that authentication mechanisms leveraging secrets are properly inventoried and governed. You do not want probabilistic guesses about where you are storing your secrets; you want proof that they are properly vaulted, rotated, and managed. GitGuardian delivers a security model that is both practical for developers and resilient against modern threats.
Deterministically Getting Security Right
Probabilistic tools are powerful for risk detection, prioritization, and context enrichment. Generative AI may accelerate development, but without deterministic guardrails, it can also accelerate risk. GitGuardian closes this gap by combining the strengths of AI-driven detection with hardened, verifiable validation for every secret, token, and non-human identity. This layered model ensures that organizations can safely leverage AI-driven insights while preserving a foundation of cryptographic certainty.
With GitGuardian, you get the best of both worlds: probabilistic intelligence where it helps, deterministic enforcement where it matters, and a security model that remains auditable, reliable, and built for the realities of modern development.
Book a demo today to see how GitGuardian can help you keep AI-powered development fast, secure, and grounded in proof; not probability.
FAQ
What are deterministic systems and how do they differ from probabilistic systems?
Deterministic systems are the bedrock of security because they leave no room for interpretation. Given the same inputs, they will always produce the same outputs, which means their behavior can be modeled, tested, and formally verified. Examples include cryptographic hash functions and network routing algorithms. Probabilistic systems, on the other hand, introduce uncertainty by design and operate within distributions rather than fixed outcomes. Large language models, Bayesian inference engines, and quantum measurements are probabilistic systems that provide confidence scores rather than binary assurances.
Why are deterministic systems essential for security enforcement?
Deterministic systems provide definitive guarantees that security teams can rely on for enforcement and auditing. Identity protocols like FIDO2 provide absolute validation that an identity is valid and a transaction is authentic. This predictability allows security teams to enforce strict controls and build trust anchors that do not drift over time. In contrast, probabilistic systems cannot offer the same binary assurances needed for trust-critical enforcement like access control or authorization decisions.
What is the difference between predictive AI and generative AI from a security perspective?
Predictive AI focuses on forecasting outcomes based on historical data and operates within predefined input-output relationships, making it easier to validate and audit. Generative AI creates new content like text, code, or images based on probabilistic reasoning, optimizing for plausibility rather than correctness. This makes generative AI more powerful but also introduces unique risks—a code assistant might suggest technically correct but fundamentally unsafe code, like hardcoded secrets or outdated libraries, without any guarantee of security compliance.
What are agentic AI systems and what new security risks do they introduce?
Agentic AI systems don't just generate or predict; they autonomously take actions, chain tasks, and interact with external systems or other agents to achieve goals. This shift from passive output to active execution expands the attack surface significantly. When their decision-making is driven by probabilistic reasoning, they can potentially bypass deterministic controls. If an AI agent is only 95% certain it shouldn't share user data, that 5% uncertainty could be exploited through clever prompts or manipulated responses to cause security breaches.
How does GitGuardian combine probabilistic and deterministic approaches for security?
GitGuardian uses probabilistic AI to improve detection accuracy—its False Positive Remover has cut false positives by up to 80%—but doesn't stop at "probably a secret." The platform validates secret candidates using deterministic methods including regular expressions and heuristics based on contextual information to eliminate uncertainty. This layered approach allows developers to benefit from AI's speed and context while maintaining the rigor of deterministic security validation that can be audited and verified.
Why can't Retrieval-Augmented Generation (RAG) systems be fully "tuned" for security?
RAG systems have a fundamental limitation: you can't completely "tune" them for security beyond scrutinizing all input and output, leaving room for mistakes that attackers can exploit. Even well-structured RAG systems operate on probabilistic reasoning rather than deterministic rules, which means they can still produce outputs that appear correct but contain security vulnerabilities. This is why probabilistic intelligence should supplement rather than replace deterministic, provable security checks.
What security risks do generative AI tools like GitHub Copilot introduce to development workflows?
Generative AI tools can accelerate productivity but also normalize insecure patterns, accidentally expose secrets, or add complexity to development pipelines. These tools optimize for plausibility rather than security correctness, potentially suggesting code that looks technically sound but violates secure coding practices. Without deterministic guardrails like automated secret scanning, dependency checks, and policy enforcement, developers risk introducing insecure code into production faster than ever before, scaling both productivity and potential vulnerabilities.
Where should probabilistic systems be used versus deterministic systems in security?
Probabilistic systems excel at risk detection, prioritization, context enrichment, pattern recognition, and anomaly detection—essentially finding signals in data. However, identity authentication, transaction authorization, cryptographic key validation, and agent permissions must be rooted in deterministic validation, not statistical confidence. Probabilistic systems are excellent for advisory layers like threat detection, but dangerous when used to define rules of access or authorization where binary assurances are required for security enforcement.
