[DOCS] Changes in Security article in docs for 24.3 (#26227)

Port from #26206

* Moved content from the Encrypted Models article to the main OpenVINO
Security article.
* Changed name of the "Intermediate Representation Suitable for INT8
Inference" article to "Low Precision IR (INT8 Inference)".

Author: msmykx-intel

docs/articles_en/documentation/openvino-ir-format/intermediate-representation-int8-inference.rst

@@ -1,6 +1,4 @@
-.. {#openvino_docs_MO_DG_prepare_model_convert_model_IR_suitable_for_INT8_inference}
-
-Intermediate Representation Suitable for INT8 Inference
+Low Precision IR (INT8 Inference)
 =======================================================
 
 

docs/articles_en/documentation/openvino-security.rst

@@ -1,21 +1,76 @@
-.. {#openvino_docs_security_guide_introduction}
-
 OpenVINO Security
-=================
+===================================================
 
+Deploying deep learning models for OpenVINO may raise security and privacy issues.
+Trained models are often valuable intellectual property and you may choose to protect them
+with encryption or other security tools.
 
-.. toctree::
-   :maxdepth: 1
-   :hidden:
+Actual security and privacy requirements depend on your unique deployment scenario.
+This section provides general guidance on using OpenVINO tools and libraries securely.
+The main security measure for OpenVINO is its
+:doc:`Security Add-on <openvino-ecosystem/openvino-security-add-on>`. You can find its description
+in the Ecosystem section.
 
-   openvino-security/openvino-encrypted-models
+.. _encrypted-models:
 
+Using Encrypted Models with OpenVINO
+##############################################
 
-Deploying deep learning models for OpenVINO may raise security and privacy issues.
-Trained models are often valuable intellectual property and you may choose to protect them with encryption or other security tools.
+Deploying deep-learning capabilities to edge devices can present security challenges like ensuring
+inference integrity, or providing copyright protection of your deep-learning models.
 
-Actual security and privacy requirements depend on your unique deployment scenario.
-This section provides general guidance on using OpenVINO tools and libraries securely.
-The main security measure for OpenVINO is its :doc:`Security Add-on <openvino-ecosystem/openvino-security-add-on>`. You can find its description in the Ecosystem section.
+One possible solution is to use cryptography to protect models as they are deployed and stored
+on edge devices. Model encryption, decryption and authentication are not provided by OpenVINO
+but can be implemented with third-party tools (i.e., OpenSSL). While implementing encryption,
+ensure that  the latest versions of tools are used and follow cryptography best practices.
+
+This guide presents how to use OpenVINO securely with protected models.
+
+Secure Model Deployment
++++++++++++++++++++++++++++++++++++
+
+After a model is optimized by model conversion API, it's deployed to target devices in the
+OpenVINO Intermediate Representation (OpenVINO IR) format. An optimized model is stored on edge
+device and is executed by the OpenVINO Runtime. TensorFlow, TensorFlow Lite, ONNX and PaddlePaddle
+models can be read natively by OpenVINO Runtime as well.
+
+Encrypting and optimizing model before deploying it to the edge device can be used to protect
+deep-learning models. The edge device should keep the stored model protected all the time
+and have the model decrypted **in runtime only** for use by the OpenVINO Runtime.
+
+.. image:: ../assets/images/deploy_encrypted_model.svg
+
+Loading Encrypted Models
++++++++++++++++++++++++++++++++++++
+
+The OpenVINO Runtime requires model decryption before loading. Allocate a temporary memory block
+for model decryption and use the ``ov::Core::read_model`` method to load the model from a memory
+buffer. For more information, see the ``ov::Core`` Class Reference Documentation.
+
+.. doxygensnippet:: docs/articles_en/assets/snippets/protecting_model_guide.cpp
+    :language: cpp
+    :fragment: part0
+
+Hardware-based protection such as Intel Software Guard Extensions (Intel SGX) can be used to protect
+decryption operation secrets and bind them to a device. For more information, see
+the `Intel Software Guard Extensions <https://software.intel.com/en-us/sgx>`__.
+
+Use the ``ov::Core::read_model`` to set model representations and weights respectively.
+
+Currently there is no way to read external weights from memory for ONNX models.
+The ``ov::Core::read_model(const std::string& model, const Tensor& weights)`` method
+should be called with ``weights`` passed as an empty ``ov::Tensor``.
+
+.. doxygensnippet:: docs/articles_en/assets/snippets/protecting_model_guide.cpp
+    :language: cpp
+    :fragment: part1
 
+Additional Resources
+####################
 
+- Intel® Distribution of OpenVINO™ toolkit `home page <https://software.intel.com/en-us/openvino-toolkit>`__.
+- :doc:`Convert a Model <legacy-features/transition-legacy-conversion-api/legacy-conversion-api>`.
+- :doc:`OpenVINO™ Runtime User Guide <../openvino-workflow/running-inference>`.
+- For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../learn-openvino/openvino-samples>`
+- For information on a set of pre-trained models, see the :doc:`Overview of OpenVINO™ Toolkit Pre-Trained Models <../omz_models_group_intel>`.
+- For IoT Libraries and Code Samples, see the `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__.

docs/articles_en/documentation/openvino-security/openvino-encrypted-models.rst

@@ -439,5 +439,5 @@ Additional Resources
 * Models in the OpenVINO IR format on `Hugging Face <https://huggingface.co/models>`__.
 * :doc:`OpenVINO™ Runtime Preprocessing <optimize-inference/optimize-preprocessing>`
 * :doc:`String Tensors <string-tensors>`
-* :doc:`Using Encrypted Models with OpenVINO <../../documentation/openvino-security/openvino-encrypted-models>`
+* :ref:`Using Encrypted Models with OpenVINO <encrypted-models>`