For AI and ML
security consultancies

A platform built for the firms that test AI and ML systems

Security consultancies that focus on artificial intelligence and machine learning systems carry a different operating burden than firms that test general SaaS or enterprise IT. Every engagement crosses the chat surface, the retrieval pipeline, the tool layer, the training data, and the underlying application, the deliverables sit alongside model risk committee material, governance reviews, and emerging regulator submissions, the report has to map findings back to the OWASP LLM Top 10, MITRE ATLAS, and the NIST AI Risk Management Framework, and the evidence chain has to survive a model version change, a guardrail revision, or a switch from a closed-weight provider to a self-hosted model without a missing link. Most adversarial-testing shops still run this delivery on a notes app, a screenshot folder, a shared chat transcript, and a ticket queue that loses context the moment the engagement closes and the model version increments.

SecPortal gives AI and ML security consultancies one workspace for engagements, findings, evidence, retests, branded delivery, and invoicing. Findings carry CVSS scores from the moment they are opened, OWASP LLM Top 10 and MITRE ATLAS tagging is part of the workflow, the client portal scopes adversarial prompts and exploit evidence behind authenticated access, and the AI-assisted reporting drafts the framework-aligned writeup the buyer is expecting. Whether the firm services an AI-native startup, an enterprise that has retrofitted assistants into existing applications, a model provider running adversarial review on its own deployments, or a panel of AI features inside a regulated platform, the platform scales without adding administrative overhead.

Capabilities AI and ML security consultancies actually use

How an AI security practice runs inside SecPortal

AI security delivery is most defensible when one operating picture covers scope, evidence, finding-to-framework mapping, retest verification across model versions, and the report. SecPortal supports the full delivery rather than a single phase of it.

From engagement kickoff to verified close, on one record

The leverage in AI security delivery is the durability of the audit chain across model version changes. SecPortal runs a single delivery flow that the next deployment, the next retest, and the next governance review can build against without reconstructing context from chat history.

1. 1Open the AI security engagement with assessed entity, in-scope models and providers, retrieval sources, connected tools, data classes the assistant can read, scope statement, rules of engagement for adversarial testing, testers, and dates stamped against the record. The rules-of-engagement template populates the standard sections; the engagement record holds the bespoke AI context.
2. 2Run the adversarial testing programme inside the engagement record. Prompt injection probes, jailbreak chains, RAG poisoning paths, agent hijack scenarios, training-data leak tests, and the surrounding application coverage (authenticated DAST, SAST, SCA) all consolidate to the same findings database, with raw outputs attached to the finding they support.
3. 3Tag each finding against the OWASP LLM Top 10 category, the MITRE ATLAS technique, or the NIST AI RMF function it impacts as it is logged. Add ISO 42001 and ISO 27001 tags where the client scope demands them. The tagging is part of the testing workflow, not a post-engagement reconciliation step.
4. 4Generate the technical report, executive summary, and remediation roadmap with AI assistance from the live record. The deliverable lands in the client portal alongside the underlying finding-level evidence (system prompt, adversarial input, model response, tool triggered), so the report and the source-of-truth point at the same data.
5. 5Run retests after the client tightens the guardrail, ships a new model version, or restructures the agent topology. Attach verification evidence to the same finding, and either close the issue with a status change actor recorded automatically or revert to open with regression notes captured in place. The audit chain stays intact for governance review and external assessor activities.

AI-specific finding classes the platform is built to handle

AI engagements surface a class of findings that do not fit the traditional web application finding template. The encyclopedia entries below cover the most common adversarial cases, with reproducibility evidence and remediation guidance the client team can act on. Each one can be tagged into a SecPortal engagement and tracked through the same workflow as a traditional pentest finding.

• The headline LLM finding class is covered in the prompt injection encyclopedia entry, including direct jailbreaks, indirect injections through retrieved documents, and payload patterns that lead to data exfiltration or tool abuse.
• Adversarial inputs that trigger pathological model behaviour fall under the regex denial-of-service entry when the surrounding application uses regex on model output, and the broader denial-of-service entry when the failure mode is resource exhaustion against the model serving layer.
• AI agents that ingest model output as a tool invocation surface the same risks covered in the server-side request forgery entry and the command injection entry, because an injected prompt can drive an agent to call internal endpoints or shell out through a tool the model controls.
• Sensitive context that leaks through model output is captured in the sensitive data exposure entry, and the hardcoded secrets entry covers cases where API keys end up inside system prompts, retrieval sources, or training data the model can be coaxed into reciting.
• Authentication and authorisation failures around AI features that bypass per-user controls are tracked under the broken access control entry and the broken object level authorization entry, because an AI assistant that calls internal APIs with elevated privileges is the same authorisation failure with a different front door.

Where AI and ML security consultancies typically start

Most AI-focused firms adopt the platform in three phases: bring the active client list and engagement records under one workspace, layer in finding-to-framework tagging and branded portal delivery, then consolidate retests, AI-assisted reporting, and invoicing onto the same record. The relevant capability and workflow pages explain each phase in detail.

• The core engagement and findings workflow lives in the penetration testing use case, and AI-heavy estates lean on the API security testing use case for the authenticated DAST coverage that surrounds AI features. Adversarial review patterned on adversary simulation lives in the red teaming use case.
• The finding-tagging model that produces framework-aligned reports sits in the findings management feature, with multi-framework mapping covered by the compliance tracking feature. Branded delivery scoped per AI-using client lives in the client portal feature, and the AI-drafted writeup produced from the live record sits in the AI reports feature.
• Coverage against the application surrounding the AI feature relies on the authenticated scanning feature for the chat and API endpoints behind login, and the code scanning feature for the agent and orchestration code that wraps the model.
• The retest workflow that pairs verification to the original finding across model versions is covered in the retesting use case, the report-delivery pattern that fits an AI-using buyer is documented in the pentest report delivery use case, and remediation cadence between formal assessments lives in the remediation tracking use case.
• Methodology references that translate cleanly onto AI scope sit in the MITRE ATT&CK framework page for the post-exploitation reasoning, the OWASP ASVS framework page for the application-layer verification standard the AI feature depends on, and the NIST SP 800-115 framework page for the testing methodology base layer most AI engagements still build on.
• The risk-management framework that AI-using clients increasingly cite at procurement and audit is covered in the NIST AI Risk Management Framework page, including the four core functions (GOVERN, MAP, MEASURE, MANAGE), the seven characteristics of trustworthy AI, the GenAI Profile (NIST AI 600-1), and the audit evidence the framework expects an engagement record to feed.

SecPortal is built for AI and ML security consultancies that want one platform for the whole adversarial-review delivery: live engagements, framework-tagged findings, evidence, retests, branded portals, AI-assisted reporting, and invoicing. AI-using clients get a deliverable that ties to the controls their governance team already tracks, and the firm gets back the hours that used to disappear into post-engagement document production and prompt-by-prompt reconciliation across chat transcripts.

If your firm is structured as a smaller partner-led practice between two and ten testers, the SecPortal for boutique security firms page covers the operating model that fits a specialist consultancy. If your firm runs a broader multi-vertical book of business, the SecPortal for cybersecurity firms page covers the multi-client delivery model. AppSec teams that run AI security review in-house can read the SecPortal for AppSec teams page for the integrated programme model, and cloud-heavy AI estates often pair this work with the model documented in the SecPortal for cloud security consultancies page.

For broader context on how AI security findings hold up after the engagement closes, the aging pentest findings research and the severity calibration research cover what happens after the report ships and the client starts working through the model and guardrail changes the engagement surfaced.