Microsoft Presidio for Consulting Decks

Key takeaway: Presidio is a genuinely good open-source library for one job: finding structured PII (names, emails, phone numbers, ID numbers) in text and images, then masking or replacing it. Consulting-deck confidentiality is mostly a different job. Most of what has to come out of a client deliverable carries no PII pattern at all: a margin figure used as a "benchmark," a strategic recommendation, a chart's underlying data, a logo's embedded URL, a codenamed deal. Presidio has no notion of any of it, and it has no native PowerPoint support, so a Presidio-based build still has to solve the hard part from scratch.

Last updated: July 2026

Microsoft Presidio is an open-source framework for detecting and de-identifying sensitive data in text, images and structured data. It combines named-entity recognition, regular expressions, rule-based logic and checksum validation to find entities like names, emails, phone numbers and government ID numbers, then masks, redacts or replaces them. It is free, it runs on your own infrastructure, and it is one of the most-used building blocks in the "let's build our own sanitisation tool" conversation happening inside consulting firms right now. It is also not built for what actually needs to come out of a consulting deck.

What Presidio actually is

Presidio ships as four components:

• Presidio Analyzer: detects PII spans in text using a mix of NER models, regex patterns, rule-based context logic and checksum validation, and scores each detection with a confidence level.
• Presidio Anonymizer: takes the Analyzer's output and applies an operator to each span: redact, mask, replace, hash or encrypt.
• Presidio Image Redactor: runs OCR (Tesseract by default) over an image, then routes the extracted text through the Analyzer/Anonymizer pipeline and redacts the matched regions.
• Presidio Structured: applies the same detection logic to tabular and semi-structured data such as CSVs and database columns.

Out of the box, the Analyzer recognises a set of global entities: credit card numbers, IBANs, crypto wallets, bank account numbers, emails, phone numbers, URLs, IP/MAC addresses, dates, locations and person names. It also covers region-specific identifiers, including US Social Security Numbers, driver's licences and ITINs, UK NHS and National Insurance numbers, and pattern packs for a growing list of other countries. A separate transformer-based model adds medical entities (conditions, medications, procedures) for healthcare use cases.

That is a well-built, actively maintained library for exactly what it says on the tin: structured PII in text. It is a sensible default for compliance teams de-identifying customer data before it hits an LLM prompt, or a data engineering team scrubbing a support-ticket export.

One thing worth knowing if you're evaluating it as a long-term platform bet: as of a June 2026 release, Presidio is transitioning out of Microsoft's direct maintenance to a community-owned project under the Data Privacy Stack organisation, with container images and documentation moving off Microsoft's infrastructure. Nothing about the code changes overnight, but it's a governance shift worth factoring into a build-vs-buy decision: you'd be building on a project whose long-term maintainer just changed.

What it catches well

If the confidential content in a slide fits a recognisable pattern (a named individual, an email address, a phone number, a government ID, a card number), Presidio finds it reliably, and its context-aware scoring, matching nearby words like "SSN:" or "phone", cuts down on false positives better than a bare regex would. For the narrow slice of consulting-deck content that actually is personal data, a contact's email on a title slide, a phone number in an org chart, it does the job.

That narrow slice is genuinely narrow. Consulting deliverables carry very little classic PII. They carry client-identifying context, financial and quantitative data, competitive intelligence, deal terms and strategic recommendations. Almost none of it is a "name, email, or ID number" pattern.

What it misses

Confidential figures with no PII shape. A margin percentage used as an "industry benchmark," an unpublished growth rate, a deal valuation, a pricing figure. These are exactly the numbers that cause cross-client contamination, one client's confidential figure resurfacing as another client's "market data", and none of them look like a credit card number or an SSN. Presidio has no recognizer for "this number is a client's private figure" because that isn't a pattern; it's a judgement about the source and context of the number. Presidio's own documentation is upfront about the limit: it can help identify sensitive data using automated detection, but there is no guarantee it finds everything, and it has no mechanism for reasoning about business context at all.

Charts. A PowerPoint chart carries its underlying data table inside the file, independent of what the picture shows. Presidio's Image Redactor works by running OCR over a flattened image and redacting the text it finds. It has no concept of an embedded chart object or the spreadsheet sitting behind it. Change the picture and the numbers are still travelling inside the file.

Hidden code-level traces. Logo URLs pointing back to a client's own site, workbook and file-path metadata carrying the client's name in plain text, off-slide or hidden text boxes. None of this is PII in Presidio's sense, and none of it is visible to an OCR pass over a rendered slide.

Qualitative narrative and strategic content. "The board is split on the acquisition." "We recommend exiting the Nordics business by Q3." A strategic recommendation, a competitive assessment, a deal codename: Presidio's NER models are trained to spot PERSON, LOCATION, ORGANIZATION-type entities in general text, not to recognise that a sentence describes a client's confidential strategy. An organisation name it does catch gets treated the same whether it's a household-name public company (fine to keep) or a confidential acquisition target (must go). Presidio has no way to tell the two apart.

PowerPoint itself. Presidio has no native document format support at all; it processes text, images and tabular data. There is no PPTX parser, no chart-object handling, no speaker-notes extraction, no master-slide awareness. Every one of those has to be built separately: extracting text runs from the OOXML structure, pulling images out for the Image Redactor pipeline, handling embedded chart XML, and, critically, writing a sanitised result back into a working, editable PowerPoint file rather than a flattened image or a PDF. Presidio gives you a detection engine for plain text. Everything about the PowerPoint format sits outside it.

Comparison

Criterion	Microsoft Presidio (as shipped)	Purpose-built for consulting
Detects structured PII (names, emails, phone numbers, IDs)	Yes	Yes
Detects confidential figures with no PII pattern (margins, benchmarks, deal terms)	No, not a recognised entity type	Yes
Sanitises chart data, not just the chart image	No, no chart-object awareness	Yes
Removes hidden code-level traces (logo URLs, file metadata)	No	Yes
Distinguishes client-confidential from public organisation/entity mentions	No, same treatment for both	Yes
Native PowerPoint (PPTX) parsing and output	No, text/image/tabular only	Yes
Ready to use out of the box for a consulting deck	No, requires custom recognizers, a PPTX extraction/reinsertion pipeline and an image pipeline, all built and maintained in-house	Yes

The build-vs-buy problem this creates

None of this is a knock on Presidio doing its job badly. It's a well-scoped library that does its one job, structured PII detection, well. The problem is what a firm actually needs when it reaches for "we'll just build something on Presidio": a team still has to write the OOXML extraction layer, write custom recognizers for client names and confidential figures with no reliable pattern to key off, build a separate chart-data pipeline Presidio has no component for, tune false-positive/false-negative thresholds across all of it, and reassemble the result into a usable PPTX rather than a flattened image. Presidio removes the need to write an NER model from scratch. It does not remove the need to solve consulting-deck confidentiality; that work starts after Presidio's part ends.

That's the build-vs-buy trade-off in one line. Presidio is a free, solid foundation for the slice of a consulting deck that looks like classic PII. The rest of it, the client-identifying context, the numbers, the charts, the narrative, is exactly the engineering problem a firm takes on itself the moment it decides to build rather than buy.

For what "buy" looks like when it's purpose-built for this problem, see the AI sanitisation software for consulting firms category page.