Regiment AI by Infopercept: From Security Alerts to Governed Action
Every SOC analyst has experienced the shift when all is calm, and you still have a dozen browser tabs open. An alert comes into the SIEM. You flip to the EDR, then to the threat intel platform, then to a ticket in the ticketing platform, then text a buddy in the meantime to check if that server is an asset or a test box. And by the time you get an answer, the real work of what to do is just beginning.
In my last blog, AI Security Operations: From Alert Detection to the Full Security Lifecycle, I wrote about why that gap exists and what changes when AI coordinates the whole lifecycle instead of answering one question at a time. I closed it by saying the idea lands better once you see it inside a real platform.
So this blog is about Regiment AI, built by Infopercept, the company where I earlier worked as a SOC analyst. Plenty of products promise autonomous security. What caught my attention here is the emphasis on accountability: what the AI did, why it did it, and who approved it. That is the concept I want to walk through, whether you are just learning how security teams work or have run one for years. If you would rather see it than read about it, you can explore the platform hands-on here.
The real problem is execution, not tooling
Most teams already own the tools: a SIEM to collect and correlate logs, EDR on endpoints, scanners, a GRC platform. Infopercept’s position is that enterprises have an execution problem. Critical decisions cross too many systems, teams and approval boundaries before risk becomes action, and rebuilding evidence for leadership or an audit is slow and costly.
What Regiment AI is, and what it is not
Regiment AI is a governed command layer that treats offense (AI pentesting), defense and compliance as one continuous system. It is described as an operating model, not another AI SOC or MDR, and it is meant to work across the estate you already own without a rip-and-replace.
Two clarifications from the FAQ matter for every reader. It is not a chatbot or a single free-roaming agent. It runs specialized agents inside explicit rules of engagement. And it is currently in early access, with Infopercept onboarding select teams before general availability.
Architecture: the governance layer sits between agents and models
Regiment AI’s architecture graphic highlights a key design choice: governance is built into the execution path. An AI firewall / LLM gateway sits between the platform and its underlying models, including AWS Bedrock and other listed LLMs. The diagram also identifies PII/DLP controls, scoped authorization, a kill switch and dual approval.
The specific difference matters because controlling an AI agent involves more than restricting the prompts it receives. The system must also govern what the agent can access, which actions it can perform and when human authorization is required.
The FAQ from regiment AI also says that agents do what the existing role-based access control (RBAC) permissions allow. In reality, if an agent acts on behalf of a user, they should not be able to access modules, data or capabilities the user does not have access to.
Selective AI: deterministic first, AI for the unknown
One line on the Regiment AI page is “Every threat needs a response. Not every response needs a human”. In practice, the platform routes each task to one of three operators:
Deterministic for certainty: playbooks, policy enforcement, machine-speed response.
AI reasoning for ambiguity: correlation, classification, hypotheses, prioritization.
Human control for consequence: high-impact decisions, exceptions, escalation, kill switch.
The step I find most useful is when an AI-found pattern is validated, it is converted into deterministic automation, so the same pattern does not need AI reasoning again. That keeps cost, latency and audit effort down over time.
Offense: Scanners & Strikers
This covers AI pentest, BAS (Breach and Attack Simulation), CART(Continuous Automated Red Teaming), ASM(Attack Surface Management) and vulnerability management. What stands out to me is the idea of proof that travels. Each finding comes with a decision trace, exploit path and evidence package, making it easier to understand how the issue was identified and validated. Instead of ending up in a standalone pentest report, these findings can feed directly into defense and governance workflows as actionable signals.
Defense: Defenders & Hunters
Here, the focus is on alert triage, enrichment, phishing analysis and case summaries through pre-built recipes, with workflows governed by policies and an audit entry recorded at each step.
This part also reminds me of my own experience working in a SOC. While creating playbooks for different attack scenarios, I often found myself thinking beyond the alert itself. How would this attack appear in the SIEM? Which logs or indicators would help an analyst identify it? What should the analyst investigate next, and what action could prevent the attack from progressing or limit its impact?
Those questions shaped how I approached playbook creation. The goal was not just to document steps for responding to an alert, but to think through the attacker’s activity from the first observable sign to the appropriate response. Could we detect the activity early enough to stop the attack before it escalated? If prevention was no longer possible, what could we do to contain it and reduce the damage? That is what makes detection and response playbooks valuable.
With a platform like Regiment AI, the interesting possibility is that validated investigation patterns could be converted into repeatable recipes, helping analysts spend less time rebuilding familiar workflows and more time handling unfamiliar or complex threats.
Compliance: Compli & Lexi
Compli and Lexi collect evidence mapped to SOC 2, ISO 27001 or custom frameworks and produce audit-ready packages on demand. If you read my post on continuous controls monitoring, this is the same pattern: policy becomes a testable requirement, a check runs against live data, and the result is stored as timestamped evidence.
Remediation: Remedy X and Remedy Y
Finding a security problem is only half the job. The next step is fixing it without creating another problem in the process. Regiment AI describes two remediation capabilities:
Remedy X focuses on containing compromised systems and closing the weaknesses attackers could exploit.
Remedy Y focuses on undoing risky code or configuration changes and restoring the system to its approved, secure state.
Both are designed to follow pre-approved procedures, with each fix limited to its intended scope, recorded for review and reversible.
For example, if a server is compromised, the response may involve containing the threat and closing the entry point. If a configuration change accidentally exposes sensitive data, the response may involve restoring the previous approved settings.
The important point is that fixing a security issue should not mean giving an AI agent unrestricted access to change anything it wants. Its actions need clear boundaries, a record of what changed and a way to recover if something goes wrong.
Rules of engagement: keeping AI under control
Giving an AI agent the ability to investigate and respond to threats is useful, but it also raises an important question: how do we make sure it does not take an action it should not?
Regiment AI describes four safeguards designed to keep its agents accountable:
Scoped authority: An agent can only access the systems, information and actions it has been permitted to use. It should not be able to wander into unrelated systems or perform unauthorized tasks.
Dual approval: Certain high-risk and offensive actions require approval from two authorized people. This adds a second layer of human oversight before potentially consequential actions proceed.
Kill switch: An authorized person can stop an agent while it is working if something appears wrong or an action needs to be halted.
Immutable record: Decisions, approvals, overrides and outcomes are recorded in a log designed to prevent tampering. This gives the team a way to review what happened and understand why.
These safeguards matter because an AI agent can make a mistake, misinterpret information or recommend an action with unintended consequences. The more authority an agent has, the more important it becomes to limit what it can do and maintain human oversight.
So, the goal is not to prevent AI from acting. It is to make sure that when AI acts, it operates within clear boundaries, people can intervene when necessary, and its actions can be examined afterward.
My Take
The idea I take away from Regiment AI is simple: speed matters, but so does being able to explain an action afterwards.
In my SOC days, the difficult part of an incident wasn’t always the first alert. It was understanding what happened, deciding what to do next, and having a clear record of the decisions made along the way. That’s why I find the focus on governed execution and accountability worth exploring.
I haven’t evaluated Regiment AI in a production environment, so this is my perspective as a former analyst exploring the platform, not a verdict on its real-world performance. Thanks to the Infopercept team for making the playground available to explore these ideas firsthand.
Now I’m curious about your take. How would you make this approach more robust, practical, and capable of solving real-world security challenges in ways we haven’t explored yet?
Have a different approach, a clever use case, or an idea that could make AI-driven security better? Drop it in the comments. If you’ve got a spark, let’s turn it into a conversation.
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