Queryable Encryption + Vector Search in the MongoDB EF Core Provider (and why it matters for .NET 9 and .NET 10)

Microsoft published a nice security-meets-search update on Jan 7, 2026: the MongoDB EF Core provider now supports Queryable Encryption (equality and range) and vector search from an EF Core style LINQ surface. If your .NET 9 or .NET 10 app already speaks EF Core fluently, this is one of those features that can reduce the amount of “special MongoDB code” that leaks into your domain layer.

Encrypted queries that still look like LINQ

Queryable Encryption is interesting because it is not just “encrypt at rest.” The point is that you can still express equality and range predicates while keeping sensitive fields encrypted.

The mapping is explicit in OnModelCreating. The post shows encryption configuration like this:

protected override void OnModelCreating(ModelBuilder modelBuilder)
{
    modelBuilder.Entity<Employee>(entity =>
    {
        entity.Property(e => e.TaxPayerId)
            .IsEncryptedForEquality(<Your Data Encryption Key GUID>));

        entity.Property(e => e.Salary)
            .HasBsonRepresentation(BsonType.Decimal128)
            // Salaries from 0 to 10 million, no decimal place precision
            .IsEncryptedForRange(0m, 10000000m, 0,
                <Your Data Encryption Key GUID>));              
    });
}

Once mapped, the queries read like normal EF Core queries:

// Encrypted Equality Query
var specificEmployee = db.Employees.Where(e => e.TaxPayerId == "45678");

// Encrypted Range Query
var seniorEmployees = db.Employees.Where(e => e.Salary >= 100000m && e.Salary < 200000m);

The big win is architectural: you can keep query intent in code reviews (who is filtering by salary, who is matching by tax id) without sprinkling ad-hoc encryption plumbing across the app.

Vector search from your DbContext

Vector search is showing up everywhere because search is shifting from keyword matching to similarity matching. The provider adds mapping for vector fields and a vector search query API.

From the DevBlogs post, you map a float array as a binary vector:

b.Property(e => e.PlotEmbedding)
   .HasElementName("plot_embedding_voyage_3_large")
   .HasBinaryVectorDataType(BinaryVectorDataType.Float32);

// OR in the model:
[BinaryVector(BinaryVectorDataType.Float32)]
public float[]? PlotEmbedding { get; set; }

Then you can query by similarity:

var similarMovies = await db.Movies.VectorSearch(
        e => e.PlotEmbedding,
        myCustom.PlotEmbedding,
        limit: 10)
    .ToListAsync();

If you are building on .NET 9 or .NET 10, this can keep your “recommendations/search” logic closer to your existing EF Core patterns, with fewer custom query pipelines to maintain.

If you want the full context and provider details, read the original post: Secure and Intelligent: Queryable Encryption and Vector Search in MongoDB EF Core Provider.