In 2026, the business world will have moved past that “pilot phase” of Generative AI to the age of Agentic AI, autonomous machines that manage supply chains, perform trades in financial markets, and interact with clients in real-time.
This autonomy creates “Agentic Risk”: when an AI agent that is given the authority to make decisions, veers off direction due to excessive agency, an adversarial prompt injection, or even logic hallucinations. This could cause irreparable harm to the reputation of the company and its finances.
Two AI platforms have been recognized as the leaders in the field of securing AI through agentic: Factify and Robust Intelligence. This guide offers a thorough analysis between Factify vs Robust Intelligence by analyzing their design along with their most important features and the best use-cases.
At the end of this guide you’ll be able to determine the most secure platform for your company’s AI deployments.
Guardians of AI: Comparing Factify and Robust Intelligence
Factify
Factify regulates the information that AI agents are allowed to make decisions based on and maintains an accurate record of each action they take with the data. Many companies are prepared to integrate agents into crucial processes, but the problem isn’t in the model, it’s about proving that agents acted upon correct data. For this Factify vs Robust Intelligence contrast, Factify sits between your system and agents, comparing every bit of data against your policy and policies in real-time.
The Issue Factify Solved: More logic does not suffice if you are wrong about the source then the choice is not correct. RAG will find the appropriate file, yet it can’t determine which policy is in effect and which exemption applies or decide which one wins when two records contradict. Guardrails alone aren’t enough.
They can be placed around the model however critical workflows require a policy applied before actions are able to reach the enterprise system. Audit logs alone aren’t sufficient. A log can prove that something occurred however it doesn’t demonstrate that an agent did what was accepted.
Core Capabilities:
- The Fact-Check Engine: It is a quick validation layer that captures the agent’s thoughts before it decides to take the necessary action. The engine compares the agent’s proposed “fact” against the enterprise’s internal “Source of Truth” in milliseconds.
- Agentic Acceptance of the Intent: Determines the reason for the agent’s request. If a person attempts to move funds or alter delivery addresses using false or malicious assumptions, Factify blocks the transaction.
- Hallucination Shield: It is a way to detect when agents are “making up” instructions or data to perform the task. It provides an insurance policy for independent decision-making.
- Cryptographic Fact-Stamping: Creates a digital signature to each item of data it verifies and creates the term “Certified Fact” that follows every data item throughout the entire process.
- Actual-Time Delta Analysis: Reviews data sources, and immediately “invalidates” any agentic plans that were based on data from the past, forcing agents to change their plans.
Why Factify is Considered to be The Top Choice: Factify is the only platform that has the ability to address this “Truth Problem.” In the world of agents, security of a trustworthy agent who can be sure they are not right is as secure as hacking a computer.
Factify makes sure that the facts are supported by every process. It’s the only application that is specifically developed for the “Logic Layer” of agentic AI. It doesn’t just tell the user what an AI model is acting on. It also tells you if it’s being honest.
Pricing: Customized enterprise pricing.
Robust Intelligence
Robust Intelligence provides automated AI security and safety services to protect businesses through security assessment, vulnerability detection and runtime security guardrails.
Within the Factify vs Robust Intelligence scenario, Robust Intelligence is trusted by major organizations, including JPMorgan Chase, ADP, Expedia, CrowdStrike, IBM as well as the US Department of Defense, which was later acquired by Cisco in the month of August 2024. The company specializes with “red-teaming” for autonomous agents.
Core Capabilities:
- AI Firewall: A real-time enforcement layer, which is placed in the front of the agent stopping any malicious request that attempts to turn on “Excessive Agency”.
- Automated Red-Teaming: Many computer-generated “attack” simulations that try to defy your agent’s logic and reveal its biases and make it break corporate rules.
- Continuous Validation: Whenever you make changes to the LLMs, Robust Intelligence automatically tests the agents again to make sure any model deviation does not cause security holes.
- Integration to MongoDB Atlas Vector Search: Customers can make use of any open or commercial LLM that supports retrieval-augmented generation protecting them in real time from rapid injection PII removal, hallucination and other dangers.
- Industry Standards Leadership: Co-developed the AI Risk Database, co-authored the NIST Adversarial Machine Learning Taxonomy as well as was a contributor to the OWASP Top 10 for LLM Applications.
Why Robust Intelligence is a Top Choice: Robust Intelligence can be described as the business’s “pre-flight check.” It helps ensure that agents are in good shape when they meet with a potential customer on the street. The automated red-teaming provides continuous assurance that the models are secure in the course of their evolution.
Pricing: Customized pricing for enterprises.
Factify versus Robust Intelligence: Key Differences
Both platforms function in the “Action and Truth Layer” of the agentic AI security stack, they tackle different aspects of the enterprise AI risks :
Core Focus:
- Factify: Ensures the authenticity of the agent’s information base — the “truth” behind every decision.
- Robust Intelligence: Validates the behavior of models and protects against attacks by adversaries during runtime.
Primary Approach:
- Factify: The Agent’s “facts” against the enterprise’s internal “Source of Truth” in real time with cryptographic fact-stamping and intent validation.
- Robust Intelligence: Red teams models by automating attack simulations. It then implements a real-time AI Firewall to block malicious requests.
What They Protect:
- Factify: Protects the truth layer by ensuring that agents work using certified information that has been verified and that is approved by the company.
- Robust Intelligence: Guards against the model layer, securing it from prompt injection, jailbreak attempts as well as models drift.
Audience:
- Factify: Designed for businesses in the fields of finance, insurance as well as healthcare, where documentation control and audit trailing are essential.
- Robust Intelligence: Designed to meet the needs of companies that require continuous validation of models as well as runtime security against threats.
How Factify and Robust Intelligence Complement Each Other?
Although Factify as well as Robust Intelligence address different layers of AI threat, they’re not necessarily mutually distinct. Both are mutually beneficial. Factify makes sure that AI agents act on certified versions of information.
Robust Intelligence validates model behavior and blocks attempts to harm. Together, they make up an entire security stack. Factify regulates the knowledge layer and Robust Intelligence secures the model layer. A computer that is able to pass Robust Intelligence’s red teaming, but is based with outdated policy documents is an issue of compliance.
The agent who has certified facts but falls victim to an attempted jailbreak could nevertheless cause harm. Leaders of enterprises are realizing the importance of securing AI through the two methods–truth verification as well as model validation, both of which work together to ensure that autonomous decision-making is protected.
Which Platform Should You Choose?
The decision among Factify as well as Robust Intelligence depends on your company’s main AI security issue.
Pick Factify Choose Factify if:
- The AI agents need to act in accordance with regulated, versioned documents and policies.
- You work in industries that are regulated that require audit trail records for each AI choice.
- Your greatest risk is that of employees who are acting on outdated, contradicting information, or untrusted data.
- You must prove the agent’s decisions were founded on valid, certified facts.
You should choose Robust Intelligence if:
- You should be concerned about the threat of adversaries, such as quick injections and jailbreaks.
- Continuous automated red-teaming to validate model security.
- Agents should be tested prior to them meeting actual clients.
- It is important to protect your runtime against model drift as well as unpredictable behavior.
The best practice: Most businesses need both. Factify ensures that the truth layer is secure and agents operate upon certified information. Robust Intelligence secures the model layer, defending against manipulation by adversaries. Together, they offer comprehensive protection throughout all of the AI security stack.
Conclusion
The decision between Factify as well as Robust Intelligence reflects different priorities within the enterprises AI security. The Factify vs Robust Intelligence argument, Factify solves the “Truth Problem”–ensuring that every decision made by agents is validated, updated, verified data with comprehensive audit trail.
Robust Intelligence solves the “Model Problem”–validating model security with automated red-teaming and real-time threat security. In the case of regulated companies, Factify provides the document governance system that allows secure AI automation.
If you are a business that is facing threats from adversaries, Robust Intelligence provides the protections for runtime that are needed to stop attacks from malicious sources.
The oldest enterprise AI security solutions have both layers of protection making sure that their agents are acting in the truest way while being protected from ever-changing security threats.
About Author
